A relaying can significantly improve performance of contemporary mobile networks in terms of capacity and/or energy consumption. Nevertheless, an incorporation of conventional relay stations into the mobile networks is usually expensive in terms of both capital and operational expenditures for the mobile operators. With an evolution of Device-to-device (D2D) communication, the relaying can be also facilitated via a user equipment (UE) by means of D2D relaying. In the D2D relaying, the users relay data for others, thus significantly cutting the mobile network operators’ expenditures. The D2D relaying also offers a plenty of opportunities for the relaying as, in theory, each UE can play a role of the relay. To fully unlock all benefits of the D2D relaying, however, there are still many challenges to be addressed, such as an efficient radio resource and interference management, mobility management, security and trust issues, or finding proper incentives to motivate users to help others. In this survey, we introduce a detailed taxonomy of the D2D relaying concept encompassing an overview of various relaying scenarios, types of relays, and radio resource management techniques to be optimized. Then, we provide a comprehensive overview of research works targeting the D2D relaying addressing the above-mentioned challenges, compare these works from various perspectives, identify their potential drawbacks and limitations, and also draw some interesting lessons for the readers. Last, based on the gaps in the current literature, we identify key open research challenges deserving further attention of the researches to make the D2D relaying feasible and attractive option for mobile network operators as well as end-users in emerging 6G networks.
Mitigation of Doppler Effect in High-speed Trains through Relaying
Provisioning high quality of service to the users on board of high-speed trains is a challenge due to strong signal attenuation of carriage, frequent and simultaneous handovers of users, and/or Doppler effect. In this paper, we propose a novel concept of data relaying via a moving relay, such as vehicle on a nearby road, to mitigate a negative impact of Doppler effect. To this end, we propose a moving relay selection algorithm considering not only the channel quality between the train, base station and the moving relays, but also a relative speed and a direction of movement. This allows us to mitigate the negative impact of Doppler effect on the communication capacity by reducing the relative speed between the train and the base station via the intermediate relay moving in the same or similar direction as the train. The simulation results demonstrate that the proposed concept is able to boost the communication capacity by up to 140% with respect to no relaying.
On Energy Consumption of Airship-Based Flying Base Stations Serving Mobile Users
Flying base stations (FlyBSs) can serve space-time varying heterogeneous traffic in the areas, where a deployment of conventional static base stations is uneconomical or unfeasible. We focus on energy consumption of the FlyBSs serving moving users. For such scenario, rotary-wing FlyBSs are not efficient due to a high energy consumption while hovering at a fixed location. Hence, we consider airship-based FlyBSs. For these, we derive an analytical relation between the sum capacity of the users and the energy spent for flying. We show theoretical bounds of potential energy saving with respect to a relative sum capacity guarantee to the users for single FlyBS. Then, we generalize the problem towards multiple FlyBSs and we propose an algorithm minimizing the energy consumption of the FlyBSs serving moving users under a constraint on the minimum relative sum capacity guarantee. The proposed algorithm reduces the energy consumed by the airship-based FlyBSs for flying by dozens of percent at a cost of only a marginal and controlled degradation in the sum capacity. For example, if the degradation in the sum capacity up to 1% is allowed, 55.4%, 67.5%, and 90.7% of the energy is saved if five, three, and one FlyBSs are deployed, respectively.
Optimal Positioning of Flying Base Stations and Transmission Power Allocation in NOMA Networks
Unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) are considered as an efficient tool to enhance the capacity of future mobile networks and to facilitate the communication in emergency cases. These benefits are, however, conditioned by an efficient control of the FlyBSs and management of radio resources. In this paper, we propose a novel solution jointly selecting the optimal clusters of an arbitrary number of the users served at the same time-frequency resources by means of non-orthogonal multiple access (NOMA), allocating the optimal transmission power to each user, and determining the position of the FlyBS. This joint problem is constrained with the FlyBS's propulsion power consumed for flying and with a continuous guarantee of a minimum required capacity to each mobile user. The goal is to enhance the duration of a communication coverage in NOMA defined as the time interval within which the FlyBS always provides the minimum required capacity to all users. The proposed solution clusters the users and allocates the transmission power of the FlyBS to the users efficiently so that the communication coverage provided by the FlyBSs is extended by 67%-270% comparing to existing solutions while the propulsion power is not increased.
Optimization of Total Power Consumed by Flying Base Station Serving Mobile Users
While the integration of flying base stations (FlyBSs) into future mobile networks has received plenty of attention, a backhaul link (i.e., the link between a static base station and the FlyBS) is often either fully disregarded or oversimplified. However, the backhaul link and an access link between the FlyBS and users should be managed together to exploit radio resources efficiently. Thus, we introduce a novel framework considering the FlyBSs with a realistic backhaul to maximize the sum capacity of the users. First, we propose a scheme for an association of the users and a transmission power allocation. Thus, we derive a closed-form expression for the optimal allocation of the FlyBSs' transmission power to individual users to utilize the radio resources at the backhaul and access links in an efficient way. Second, we develop an algorithm for a repositioning of the FlyBSs and a reallocation of the FlyBSs' transmission power to further improve the overall sum capacity. Third, we design a scheme reusing the access links by multiple users in the coalitions to reduce the FlyBSs' transmission power. The reduced transmission power allows to further increase the sum capacity of the users via an additional repositioning of the FlyBSs. Alternatively, the reduced transmission power also lowers the level of interference experienced by the underlying devices not communicating via the FlyBSs. Our proposal increases the sum capacity of the users by up to 60% while suppressing the interference to the underlying devices by up to 7.7 dB compared to the state-of-the-art schemes.
Q-Learning-based Setting of Cell Individual Offset for Handover of Flying Base Stations
Flying base stations (FlyBSs) are widely used to improve coverage and/or quality of service for users in mobile networks. To ensure a seamless mobility of the FlyBSs among the static base stations (SBSs), an efficient handover mechanism is required. We focus on the handover of FlyBSs among SBSs and we dynamically adjust the cell individual offset (CIO) of the SBSs based on their load to increase the sum capacity of the users served by the FlyBSs while considering also a handover cost. Due to complexity of the defined problem and limited knowledge of other parameters required for conventional optimization methods, we adopt Q-learning to solve the problem. For Q-learning, we define a reward function reflecting the tradeoff between the capacity of users and the cost of performed handovers. The proposed Q-learning based approach converges promptly and increases the sum capacity of the users served by the FlyBSs by up to 23% for eight deployed FlyBSs comparing to state-of-the-art algorithms. At the same time, the number of handovers performed by the FlyBSs is notably reduced (up to 25%) by the proposal.
QoS-Aware Sum Capacity Maximization for Mobile Internet of Things Devices Served by UAVs
The use of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) is considered as an effective tool to improve performance of the mobile networks. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we maximize the sum downlink capacity of the mobile Internet of Things devices (IoTD) served by the FlyBSs while a minimum required capacity to every device is
guaranteed. To this end, we propose a geometrical approach allowing to derive the 3D positions of the FlyBS over time as the IoTDs move and we determine the transmission power allocation for the IoTDs. The problem is formulated and solved under practical constraints on the FlyBS’s transmission and propulsion power consumption as well as on flying speed. The proposedsolution is of a low complexity and increases the sum capacity
by 15%-46% comparing to state-of-the-art works.
Sum Capacity Maximization in Multi-Hop Mobile Networks with Flying Base Stations
Deployment of multi-hop network of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) presents a remarkable potential to effectively enhance the performance of wireless networks. Such potential enhancement, however, relies on an efficient positioning of the FlyBSs as well as a management of resources. In this paper, we study the problem of sum capacity maximization in an extended model for mobile networks where multiple FlyBSs are deployed between the ground base station and the users. Due to an inclusion of multiple hops, the existing solutions for two-hop networks cannot be applied due to the incurred backhaul constraints for each hop. To this end, we propose ananalytical approach based on an alternating optimization of the FlyBSs’ 3D positions as well as the association of the users to the FlyBSs over time. The proposed optimization is provided under practical constraints on the FlyBS’s flying speed and altitude as well as the constraints on the achievable capacityat the backhaul link. The proposed solution is of a low complexity and extends the sum capacity by 23%-38% comparing to state-of-the-art solutions.
Dynamic Adjustment of Scheduling Period in Mobile Networks Based on C-RAN
The cloud radio access network (C-RAN) is considered as one of the compelling architectures to meet requirements of the future mobile networks in term of delivering cutting-edge applications and increasing the network flexibility. However, one of the key challenges to cope with in the C-RAN is the fronthaul delay between the baseband unit (BBU) and remote radio head (RRH). The fronthaul delay impacts negatively on various radio resource management techniques including scheduling. The scheduling suffers from the fronthaul due to an additional delay between the time when inputs for the scheduling are provided by the users and the time when the new scheduling is applied. In this paper, we propose a dynamic adjustment of the centralized scheduling period aiming to suppress the negative impact of the fronthaul delay and to increase the network throughput. To this end, we propose two algorithms estimating the scheduling period for individual users: i) from previous channel quality information, and ii) via a prediction of the future channel quality. Simulation results show that both proposed solutions not only provide a network throughput close to the theoretical upper bound but also outperform existing approaches.
Dynamic Allocation of Computing and Communication Resources in Multi-Access Edge Computing for Mobile Users
The Multi-Access Edge Computing (MEC) constitutes computing over virtualized resources distributed at the edge of mobile network. For mobile users, an optimal allocation of communication and computing resources changes over time and space, and the resource allocation becomes a complex problem. Moreover, for delay constrained applications, the resource allocation to mobile users cannot be solved by approaches designed for static users, as a solution would not be obtained within a desired time. Thus, in this paper, we propose a low-complexity computing and communication resource allocation for offloading of real-time computing tasks generated with a high arrival rate by the mobile users. We exploit probabilistic modeling of the users’ movement to pre-allocate the computing resources at base stations and to select suitable communication paths between the users and the base station with the pre-allocated computing resources. The simulations show that the proposed algorithm keeps the offloading delay below 100 ms for the small tasks even with the arrival rate of five tasks per second per user, while the state-of-the-art algorithms can handle only up to 0.5 tasks per second per user. Thus, the proposal enables an exploitation of the MEC for various real-time applications even if the users are moving.
Energy Consumption Performance of Opportunistic Device-to-Device Relaying Under Log-Normal Shadowing
Efficient transmission protocols are required to minimize the energy consumption of mobile devices for ubiquitous connectivity in the next-generation of wireless networks. In this article, we analyze the energy consumption performance of a two-hop opportunistic device-select relaying (ODSR) scheme, where a device can either transmit data directly to a base station (BS) or relay the data to a nearby device, which forwards the data to the BS. We select a single device opportunistically from a device-to-device (D2D) network based on the energy required for transmission, including the energy consumed in the circuitry of the devices. By considering the log-normal shadowing as the dominant factor between devices and the BS, and Rayleigh fading in D2D links, we derive analytical bounds and scaling laws on average energy consumption. The derived analytical expressions show that the energy consumption of the ODSR decreases logarithmically with an increase in the number of devices, and achieves near-optimal performance only with a few nearby devices. This is an important design criterion to reduce latency and overhead energy consumption in a relay-assisted large-scale network. We also demonstrate the performance of the ODSR using simulations in realistic scenarios of a wireless network.
Hierarchical Scheduling for Suppression of Fronthaul Delay in C-RAN with Dynamic Functional Split
The cloud radio access network (C-RAN) can potentially reduce a network's deployment cost and energy consumption. However, a connection between a centralized baseband unit and distributed remote radio heads, known as a fronthaul, introduces an additional delay to both control and user planes. This delay is a serious limiting factor for radio resource management functionalities, such as scheduling, because the radio resources are assigned to users according to outdated channel quality information if the fronthaul delay is non-zero. This article provides an overview of existing scheduling approaches suitable for C-RAN and identifies their potential limitations. Based on these limitations, we outline a framework for hierarchical scheduling. The hierarchical scheduling mitigates a negative impact of the fronthaul delay on the throughput of non-cell-edge users and enables efficient retransmission of erroneous data. Besides, cell-edge users can still benefit from interference mitigation techniques requiring centralized control. We compare individual scheduling approaches and show that hierarchical scheduling increases the network throughput (by up to 26 percent) and reduces the number of retransmissions with respect to the existing solutions.
Device-to-device (D2D) relaying is a concept, where some users relay data of cell-edge users (CUEs) experiencing a bad channel quality to a base station. While this research topic has received plenty of attention, a critical aspect of the D2D relaying remains a selfish nature of the users and their limited willingness to relay data for others. Thus, we propose a scheme to identify potential candidates for the relaying and provide a sound incentive to these relaying users (RUEs) to motivate them helping other users. First, we provide a detailed theoretical analysis showing when and if the relaying is beneficial for the CUE(s) and related RUE. Second, to choose among all possible incentive-compliant relaying options, we formulate the optimal CUE-to-RUE matching problem maximizing a network-wide performance. Since the optimal solution is hard to obtain for a high number of users, we propose a low-complexity greedy algorithm and prove its constant worst-case approximation guarantees to the optimum. Finally, we derive a closed-form expression for a fair allocation of the resources among the CUEs and the RUEs. The proposed framework more than doubles the users' capacity and/or reduces the energy consumption by up to 87% comparing to existing incentive-based relaying schemes.
Optimization of Cell Individual Offset for Handover of Flying Base Station
Flying base stations (FlyBSs) mounted on unmanned aerial vehicles (UAVs) are widely used in mobile networks to improve a coverage and/or quality of service for users. To ensure a seamless mobility of the FlyBSs among the static base stations (SBSs), an efficient handover mechanism is required. In this paper, we develop a novel handover mechanism determining the serving SBS for the FlyBS in order to increase the sum capacity of the users served by the FlyBS. We propose to dynamically optimize the handover by adjusting the cell individual offset of the SBS via Q-learning. The results show that the Q-learning converges promptly and the proposed approach increases the users capacity (by up to 18%) and their satisfaction with required minimum capacity (by up to 20%) comparing to state-of-the-art algorithms.
PADSA: Priority-Aware Block Data Storage Architecture for Edge Cloud Serving Autonomous Vehicles
An efficient Input/Output (I/O) caching mechanism for data storage can deliver the desired performance at a reasonable cost to edge nodes serving autonomous vehicles. Current storage caching solutions are proposed to address common applications for autonomous vehicles that are less demanding in terms of the latency (e.g., map or software upgrades). However, a serious revision of these solutions is necessary for autonomous vehicles, which rely on safety- and time-critical communication for services, such as collision avoidance, requiring very low latency. In this paper, we propose a three-level storage caching architecture for virtualized edge cloud platforms serving autonomous vehicles. This architecture prioritizes safety-critical services and allocates the two top-level caches of Dynamic Random Access Memory (DRAM) and Non-Volatile Memory (NVM) to the top priority services. We further evaluate optimum cache space allocated to each service to minimize the average latency. The experimental results show that the proposed architecture reduces the average latency in safety-critical applications by up to 70% compared to the state-of-the-art.
Positioning and Association Rules for Transparent Flying Relay Stations
Transparent flying relay stations (FlyRSs), represented by transparent relays mounted on unmanned aerial vehicles (UAVs), have the potential to improve cellular network’s capacity and coverage at little extra complexity and energy cost, especially when compared with non-transparent relays. As the transparent relays do not transmit reference signals, they do not lend themselves easily to channel estimation. This makes solving the problems of user association and positioning of transparent FlyRSs much harder. We propose a solution enabling an efficient association of users to the FlyRSs and determining suitable positions of the FlyRSs. Surprisingly, this can be done knowing neither the qualities of the channels linking the FlyRSs and the users nor the users’ location information. Our approach involves the users being grouped into clusters based on the channels to nearby static base stations via agglomerative hierarchical clustering. Then, 3D positions of one FlyRS per cluster are determined by deep neural networks. The proposal improves the users’ sum capacity with respect to existing solutions that rely on the knowledge of users’ positions.
Reuse of Multiple Channels by Multiple D2D Pairs in Dedicated Mode: Game Theoretic Approach
Device-to-device communication (D2D) is expected to accommodate high data rates and to increase the spectral efficiency of mobile networks. The D2D pairs can opportunistically exploit channels that are not allocated to conventional users in a dedicated mode. To increase the sum capacity of D2D pairs in the dedicated mode, we propose a novel solution that allows the reuse of multiple channels by multiple D2D pairs. In the first step, the bandwidth is split among D2D pairs so that each pair communicates at a single channel that guarantees a minimal capacity for each pair. Then, the channel reuse is facilitated via a grouping of the D2D pairs into coalitions. The D2D pairs within one coalition mutually reuse the channels of each other. We propose two approaches for the creation of the coalitions. The first approach reaches an upper-bound capacity by optimal coalitions determined by the dynamic programming. However, such approach is of a high complexity. Thus, we also introduce a low-complexity algorithm, based on the sequential bargaining, reaching a close-to-optimal capacity. Moreover, we also determine the transmission power allocated to each reused channel. Simulations show that the proposed solution triples the sum capacity of the state-of-the-art algorithm with the highest performance.
Selection of Relays in Multi-Pair D2D Communication with Multicast
This paper investigates Device-to-Device (D2D) communication with multiple user equipments (UEs) acting as sources intending to transmit information to their respective destinations via intermediate relay represented by, e.g., UEs or unamnned aerial vehicles (UAVs). We propose a novel solution for the selection of the relays serving multiple D2D pairs. We allow multiple D2D pairs to use the same relay while the D2D pairs reuse the same channel to increase spectrum usage. To mitigate interference, we exploit a multicast transmission with precoding to distinguish the transmission for each D2D pair and to increase the system capacity. The simulations demonstrate that our proposed scheme provides a significant gain (up to two times) in terms of capacity compared to the competitive schemes.
Soft Frequency Reuse With Allocation of Resource Plans Based on Machine Learning in the Networks With Flying Base Stations
Flying base stations (FlyBSs) enable ubiquitous communications in the next generation mobile networks with a flexible topology. However, a deployment of the FlyBSs intensifies interference, which can result in a degradation in the throughput of cell-edge users. In this paper, we introduce a flexible soft frequency reuse (F-SFR) that enables a self-organization of a common SFR in the networks with an unpredictable and dynamic topology with the FlyBSs. We propose a graph theory-based algorithm for an allocation of resource plans, which is understood as a bandwidth allocation and a transmission power setting in the context of SFR. Furthermore, we introduce a low-complexity implementation of the proposed resource allocation using deep neural network (DNN) to significantly reduce the computation complexity. We show that the proposed F-SFR increases the throughput of cell-edge users by 16% to 26% and, at the same time, improves the satisfaction of the cell-edge users by up to 25% compared to the state-of-the-art solutions. We also demonstrate that the proposed scheme ensures a higher fairness in the throughput among the users with respect to the state-of-the-art solutions. The implementation via DNN also outperforms all state-of-the-art solutions despite its very low complexity.
6G in the sky: On-demand intelligence at the edge of 3D networks
Sixth generation will exploit satellite, aerial, and terrestrial platforms jointly to improve radio access capability and unlock the support of on-demand edge cloud services in three-dimensional (3D) space, by incorporating mobile edge computing (MEC) functionalities on aerial platforms and low-orbit satellites. This will extend the MEC support to devices and network elements in the sky and forge a space-borne MEC, enabling intelligent, personalized, and distributed on-demand services. End users will experience the impression of being surrounded by a distributed computer, fulfilling their requests with apparently zero latency. In this paper, we consider an architecture that provides communication, computation, and caching (C3) services on demand, anytime, and everywhere in 3D space, integrating conventional ground (terrestrial) base stations and flying (non-terrestrial) nodes. Given the complexity of the overall network, the C3 resources and management of aerial devices need to be jointly orchestrated via artificial intelligence-based algorithms, exploiting virtualized network functions dynamically deployed in a distributed manner across terrestrial and non-terrestrial nodes.
Deep Learning for Selection Between RF and VLC Bands in Device-to-Device Communication
This letter focuses on the selection between radio frequency (RF) and visible light communications (VLC) bands for users exchanging data directly with each other via device-to-device (D2D) communication. We target to maximize the energy efficiency of D2D communication while the outage is minimized. Since the VLC channel can vary quickly due to the possible changes in irradiance and incidence angles, we aim to reach a quick band selection decision in a multi-user scenario based only on the knowledge of the received power and sum interference from all D2D transmitters at the individual D2D receivers. The proposed solution is based on a deep neural network making an initial band selection decision. Then, based on the DNN's output, a fast heuristic algorithm is proposed to further improve the band selection decision. The results show that the proposal reaches a close-to-optimal performance and outperforms the existing solutions in complexity, outage ratio, and energy efficiency.
Energy Efficient Positioning of Flying Base Stations via Coulomb’s law
In this paper, we propose an algorithm for an initial
positioning of the unmanned aerial vehicles (UAVs) acting as
flying base stations (FlyBSs). We target to maximize the FlyBSs
deployment efficiency in terms of the number of users satisfied
with an experienced data rate and the energy consumed by the
FlyBSs to fly to their initial positions. The required data rates
of the users, energy consumption of the FlyBSs, and mutual
interference among all base stations and users are represented
as electrical forces and the initial positions of the FlyBSs are
determined via Coulomb’s law. In comparison to the state of
the art algorithms, the efficiency of the FlyBS deployment is
improved at least 2.5 times and, at the same time, the users’
satisfaction with the experienced data rate is increased between
6 to 10 %.
Flexible Soft Frequency Reuse for Interference Management in the Networks with Flying Base Stations
The low cost, fast deployment, and flexible network topology of mobile networks with flying base stations (FlyBSs) make the FlyBSs a promising solution for ubiquitous communications in next generation mobile networks. However, an agility of the FlyBSs intensifies interference, which results in a notable degradation in throughput of cell-edge users. In this paper, we introduce a flexible soft frequency reuse (F-SFR) that enables a self-organization of a common SFR. Thus, the proposed F-SFR can handle an unpredictable and a dynamic topology of the networks with FlyBSs. To this end, we propose a graph theory-based algorithm for allocation of resources, which is understood as a bandwidth allocation and a transmission power setting in the context of SFR. We show that the proposed F-SFR can achieve 16% to 26% improvement in the throughput of the cell-edge users and improves the satisfaction of the cell-edge users up to 25% compared to the state of the art SFR solutions. We also demonstrate that the proposed scheme ensures a higher fairness in throughput among the users.
Integrating UAVs as Transparent Relays into Mobile Networks: A Deep Learning Approach
Since flying base stations (FlyBSs) are energy constrained, it is convenient for them to act as transparent relays with minimal communication control and management functionalities. The challenge when using the transparent relays is the inability to measure the relaying channel quality between the relay and user equipment (UE). This channel quality information is required for communication-related functions, such as the UE association, however, this information is not available to the network. In this letter, we show that it is possible to determine the UEs' association based only on the information commonly available to the network, i.e., the quality of the cellular channels between conventional static base stations (SBSs) and the UEs. Our proposed association scheme is implemented through deep neural networks, which capitalize on the mutual relation between the unknown relaying channel from any UE to the FlyBS and the known cellular channels from this UE to multiple surrounding SBSs. We demonstrate that our proposed framework yields a sum capacity that is close to the capacity reached by solving the association via exhaustive search.
Joint Association, Transmission Power Allocation and Positioning of Flying Base Stations Considering Limited Backhaul
An integration of flying base stations (FlyBSs) into future mobile network allows to manage scenarios with a highly varying density and requests of user equipments (UEs). While this research topic has received plenty of attention, a backhaul link quality (i.e., the link between a static base station and FlyBS) is either fully disregarded or oversimplified. Nevertheless, to exploit radio resources efficiently, the backhaul link and an access link (i.e., the link between the FlyBS and UE) should be managed together. Thus, in this paper, we introduce a novel power efficient and backhaul-aware association of the UEs to either the FlyBSs or the SBSs to maximize the sum capacity of all UEs. The association of UEs is managed joinlty with the transmission power allocation and the UEs are associated according to the transmission power required at the FlyBSs to serve the UEs and the benefits observed by each UE if it is associated to the particular base station. In this regard, we derive a closed-form expression for the optimal allocation of the FlyBSs' transmission power to individual UEs to exploit the radio resources at backhaul and access links efficiently. Then, the proposed framework is enhanced by a re-positioning of the FlyBSs and a subsequent re-allocation of the transmission power at the FlyBSs to further improve the overall sum capacity. The simulations show that our proposal significantly increases the sum capacity of the UEs (from 19.6% to 135.3%) with respect to state of the art schemes.
Low-Complexity Iterative Soft-output Demodulation for Hierarchical Quadrature Amplitude Modulation
This paper proposes a novel design of low-complexity soft-output demodulation and soft-output demapping for multi-level iterative decoding of any double-binary code and high-order hierarchical quadrature amplitude modulation (HQAM) schemes. The proposed solution exploits two techniques of self-interference cancellation. The fist one, a blind successive self-interference cancellation, provides a coarse synchronization in an acquisition mode of a receiver. The second one, a hard decision directed parallel self-interference cancellation, is exploited in a tracking mode. The proposed solution is of a very low complexity corresponding only to QPSK demodulation even for modulations of higher orders. Such low complexity allows an efficient implementation of HQAM in mobile and wireless networks with no signaling or coordination between transmitter and receiver required for a selection of modulation. Thus, the proposed approach is suitable for many up-to-date solutions including communication via drones, transparent relaying, or device-to-device communication. The designed solution is verified via a reference implementation of 256-HQAM scheme in FPGA. The results confirm a suitability of the proposed scheme for HQAM demodulation and show that a low bit error rate is achieved by the proposed solution in a wide range of signal to noise ratio.
Mobility management for D2D communication combining radio frequency and visible light communications bands
Combination of radio frequency (RF) and visible light communication (VLC) bands for device-to-device (D2D) communication is seen as a promising way to both increase the system capacity and cope with an overcrowded RF bands. The main concern, however, is a proper mobility management and selection of the band that is beneficial at the moment. While the VLC usually provides a much higher throughput than RF, it is also very sensitive to a signal blockage and shadowing. Therefore, throughput as well as potential sudden drops in VLC channel quality should be considered in a design of handover between VLC and RF to avoid redundant handovers. In this paper, we propose an algorithm, tailored for D2D communication, deciding whether or not it is beneficial for a user equipment to switch from VLC to RF or vice versa. If handover to RF is not beneficial at the moment despite a drop in VLC channel quality, a dwell timer waits for a specific time if VLC channel recovers. We propose an optimization of the dwell timer according to estimated throughput in RF and VLC and delay due to handover. Simulations show that the proposed algorithm increases an average throughput when compared to existing state-of-the-art algorithms while number of handovers and average interruption are still very low.
Optimization of Transmission Power for NOMA in Networks with Flying Base Stations
Deployment of unmanned aerial vehicles (UAVs) as flying base stations (FlyBSs) is considered as an efficient tool to enhance capacity of mobile networks and to facilitate
communication in emergency cases. The improvement provided by such network requires a dynamic positioning of the FlyBSs with respect to the mobile users. In this paper, we focus on an optimization of transmission power of the FlyBS in networks with
non-orthogonal multiple access (NOMA). We propose a solution jointly positioning the FlyBS and selecting the optimal grouping of users for NOMA in order to minimize the FlyBS’s transmission power under the constraint on guaranteeing a minimum required
capacity for the mobile users. Moreover, we derive the grouping of users corresponding to the optimal transmission power in a low-degree polynomial time, which makes it suitable for realtime applications. According to the simulations, the proposed method brings up to 31% of FlyBS’s transmission power saving compared to existing solutions.
Optimizing Transmission and Propulsion Powers for Flying Base Stations
Unmanned aerial vehicles acting as flying base stations (FlyBSs) have been considered as an efficient tool to enhance capacity of mobile networks and to facilitate communication in emergency cases. The enhancement provided by such network necessitates a dynamic positioning of the FlyBSs with respect to the users. Despite that, the power consumption of the FlyBS remains an important issue to be addressed due to limitations on the capacity of FlyBS's batteries. In this paper, we propose a novel solution combining a transmission power control and the positioning of the FlyBS in order to ensure quality of service to the users while minimizing total consumed power of the FlyBS. We derive a closed-form solution for joint transmission and propulsion power optimization in a single future step. Moreover, we also provide a numerical method to solve the joint propulsion and transmission power optimization problem when a realistic (i.e. inaccurate) prediction of the users' movement is available. According to the simulations, the proposed scheme brings up to 26% of total FlyBS's power saving compared to existing solutions.
Predicting Device-to-Device Channels From Cellular Channel Measurements: A Learning Approach
Device-to-device (D2D) communication, which ena- bles a direct connection between users while bypassing the cellular channels to base stations (BSs), is a promising way to offload the traffic from conventional cellular networks. In D2D communication, optimizing the resource allocation requires the knowledge of D2D channel gains. However, such knowledge is hard to obtain at reasonable signaling costs. In this paper, we show this problem can be circumvented by tapping into the information provided by the estimated cellular channels between the users and surrounding BSs as these channels are estimated anyway for a normal operation of the network. While the cellular and D2D channel gains exhibit independent fast fading behavior, we show that average gains of the cellular and D2D channels share a non-explicit relation, which is rooted into the network topology, terrain, and buildings setup. We propose a deep learning approach to predict the D2D channel gains from seemingly independent cellular channels. Our results show a high degree of convergence between the true and predicted D2D channel gains. Moreover, we demonstrate the robustness of the proposed scheme against environment changes and inaccuracies during the offline training. The predicted gains allow to reach a near-optimal capacity in many radio resource management algorithms.
Reducing Energy Consumed by Repositioning of Flying Base Stations Serving Mobile Users
Unmanned Aerial Vehicles (UAVs), acting as flying
base stations (FlyBSs), are seen as a promising solution for future
mobile networks, as the FlyBSs can serve space and time varying
heterogeneous traffic in areas where deployment of conventional
static base stations is uneconomical or infeasible. However, an
energy consumption of the FlyBSs is a critical issue. In this
paper, we target a scenario where the FlyBSs serve slowly moving
users, e.g., visitors of an outdoor music festival or a performance.
In such scenario, rotary-wing FlyBSs are not efficient due to a high energy consumption while not moving (given by an effect
of a ”helicopter” dynamics). Hence, we consider small airships
or balloons. We develop a closed-form solution that determines
new positions of the FlyBSs so that the energy consumption
for a movement of the FlyBSs is reduced significantly (by 45-
94% depending on the number of deployed FlyBSs) while sum
capacity of the users is decreased only marginally (less than 1%
for before-mentioned energy savings). Moreover, the proposed
solution does not require any prediction of users’ movement,
thus, it is not affected by the prediction error or uncertainty of
the users’ behavior.
Sequential Bargaining Game for Reuse of Radio Resources in D2D Communication in Dedicated Mode
Device-to-device communication (D2D) is expected
to accommodate high data rates and to increase the spectral
efficiency of mobile networks. We focus on a dedicated mode
where D2D pairs exploit channels that are different from the
channels allocated to conventional cellular users. Such mode
is suitable for scenarios with crowded areas with many D2D
pairs where interference management between cellular and D2D
users would be very complicated. We propose a novel solution
that enables a reuse of multiple channels by multiple D2D
pairs in order to increase throughput of the D2D users. The
proposed channel reuse is facilitated via grouping D2D pairs
into coalitions. The D2D pairs within the same coalition then
mutually reuse the channels of each other. The coalitions are
defined via sequential bargaining games played among the D2D
pairs. The coalitions are created if individual D2D pairs involved
in the game benefit from participation in the coalition. The
proposed algorithm based on sequential bargaining reaches a gain in throughput of 2864% comparing to the best performing
Efficient Exploitation of Radio Frequency and Visible Light Communication Bands for D2D in Mobile Networks
The concept of device-to-device (D2D) communication, combining common radio frequency (RF) and visible light communication (VLC), is seen as a feasible way how to cope with spectrum crunch in the RF domain and how to maximize spectral efficiency in general. In this paper, our objective is to decide when RF should be utilized or if VLC proves to be the more profitable option. The selection between RF and VLC is defined as a multi-objective optimization problem targeting primarily to minimize the outage ratio while the secondary objective is to maximize the sum capacity of D2D pairs, composed by D2D transmitters and D2D receivers. To solve this problem, we design a centralized low-complexity heuristic algorithm selecting either RF or VLC band for each D2D pair relying on the mutual interference among the pairs. For interpretation of the mutual interference among the D2D pairs, we exploit directed weighted graphs adopted from the graph theory. The simulation results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of the outage ratio, sum capacity and average energy efficiency. What is more, despite a very low complexity, the proposed algorithm reaches a close-to-optimum performance provided by the exhaustive search algorithm.
Incentive Mechanism and Relay Selection for D2D Relaying in Cellular Networks
2019 IEEE Global Communications Conference (GLOBECOM) - Proceedings. San Francisco: American Institute of Physics and Magnetic Society of the IEEE, 2019. ISSN 2576-6813. ISBN 978-1-7281-0962-6.
The performance of the cell edge users (CUEs) can be improved if they transmit their data via suitable relay UEs (RUEs) exploiting device-to-device (D2D) communication. The
critical aspect of the whole relaying concept is to offer convenient incentives for the RUEs to motivate them to act as relays. The contribution of this paper is twofold. First, we propose a new incentive mechanism for the RUEs that can exploit certain amount of resources allocated to the CUE. Depending on the preferences of users, the CUEs/RUEs can benefit from relaying in terms of capacity enhancement, reduction of energy consumption or both. In this respect, we provide a detailed analysis on how and when relaying is of benefit for both sides. Second, we propose a low-complexity greedy relay selection algorithm incorporating the incentive mechanism that increases capacity up to 32.1% and/or reduces energy consumption by up to 36.1% when compared to state-of-the-art schemes. Moreover, we show that the greedy approach gives close-to-optimal performance.
Joint Positioning of Flying Base Stations and Association of Users: Evolutionary-Based Approach
Time-varying requirements of users on communication push mobile operators to increase density of base stations. However, the dense deployment of conventional static base stations (SBSs) is not always economical, for example, when periods of peak load are short and infrequent. In such cases, several Fying base stations (FlyBSs) mounted on unmanned aerial vehicles can be seen as a convenient substitution for the dense deployment of SBSs. This paper focuses on maximization of user satisfaction with provided data rates. To this end, we propose an algorithm that associates users with the most suitable SBS/FlyBS and finds optimal positions of all FlyBSs. Furthermore, we investigate the performance of two proposed approaches for the joint association and positioning based on the genetic algorithm (GA) and particle swarm optimization (PSO). It is shown that both solutions improve the satisfaction of users with provided data rates in comparison with a competitive approach. We also demonstrate trade-offs between the GA and the PSO. While the PSO is of lower complexity than the GA, the GA requires a slightly lower number of active FlyBSs to serve the users.
Joint Positioning of UAV and Power Control for Flying Base Stations in Mobile Networks
2019 IEEE Global Communications Conference (GLOBECOM) - Proceedings. San Francisco: American Institute of Physics and Magnetic Society of the IEEE, 2019. ISSN 2576-6813. ISBN 978-1-7281-0962-6.
We consider a mobile network with users seeking
to engage in a device-to-device (D2D) communication. Two D2D
users (DUEs), a transmitter and a receiver, compose one D2D
pair.We assume that the D2D pairs reuse a single communication
channel to increase the spectral efficiency. Thus, a power control
is needed to manage interference among the D2D pairs and
to maximize capacity. We address the problem of D2D power
control in the case when only standard cellular channel gains
between the DUEs and base stations (BSs) are known while
channel gains among DUEs are not available at all. We exploit
supervised machine learning to determine transmission powers
for individual D2D pairs. We show that the cellular channel
gains can, in fact, be exploited to predict the transmission power
setting for D2D pairs and, still, close-to-optimum sum capacity of
the D2D pairs is reached. Moreover, even if our proposed power
control requires no knowledge of the channel gains among DUEs
and, thus, introduces no additional signalling, the sum capacity
can be increased by 16% to 41:9% with respect to no power
control, as demonstrated via simulations.
Index Terms—Device-to-device; Power
Nash Bargaining Solution for Cooperative Relaying Exploiting Energy Consumption
In this letter, we propose a resource allocation for cooperative relaying in a scenario with a high number of communicating devices. The proposed resource allocation is based on Nash bargaining solution (NBS) and leads to a natural cooperation among devices. The NBS provides an allocation of time intervals maximizing the number of transmitted packets considering energy consumption of devices. The derived NBS is in closed form, thus, it is suitable for wireless communications with time-varying channels as no iterations are needed to find the optimum allocation. Furthermore, linear complexity of the derived NBS allows its application to future mobile networks with a high number of communicating devices.
Positioning of Flying Base Stations to Optimize Throughput and Energy Consumption of Mobile Devices
Requirements on future mobile networks call for flexible, dynamic, and scalable solutions adopted for communications. Flying Base Stations (FlyBSs) are seen as a promising way for provisioning of a connectivity to user equipments (UEs) in highly dynamic scenarios. In this paper, we focus on a positioning of multiple FlyBSs providing communication services to moving UEs in a scenario with existing deployment of static base stations. Positions of the FlyBSs are adjusted over time as the UEs move considering not only a throughput of the UEs, but also an energy consumption of the UEs. The proposed solution for the positioning of FlyBSs is based on genetic algorithms. We show that the throughput experienced by the mobile users is significantly increased (by up to 260%) by our proposed algorithm comparing to the state of the art solution. At the same time, we demonstrate that the FlyBSs notably reduce the energy consumption of the UEs for communication and the proposed positioning of the FlyBSs even emphasizes this benefit. The developed positioning algorithm converges quickly enough to be applied in real networks. These findings open a space for variety of new applications of the FlyBSs in future energy efficient wireless communication systems.
Relay Selection exploiting Genetic Algorithms for Multi-Hop Device-to-Device Communication
Device-to-device (D2D) communication allows a direct transmission between two devices. In this way, cellular user equipment’s are
not always obliged to route the data conventionally through a cellular base station. This paper focuses on multi-hop D2D communication,
where D2D relays are exploited to delivery of data from a source to a destination. We propose a novel algorithm that finds the most suitable
path between the D2D source and destination so that the capacity of
multi-hop communication is maximized. The appropriate route is found
via Genetic Algorithm (GA) with an ordered crossover. The simulation
results show that the proposed algorithm improves the capacity of multihop D2D communication from a source to a destination compared to an
existing relay selection algorithm by 20-61%. We also show that the proposed solution converges fast enough to be beneficial even in realistic
Resource Allocation for D2D Communication With Multiple D2D Pairs Reusing Multiple Channels
In this letter, the goal is to maximize sum capacity of device-to-device (D2D) communication through a reuse of each radio channel by multiple D2D pairs while each D2D pair can access multiple channels. Since existing approaches cannot be easily extended to enable reuse of multiple channels by multiple D2D pairs in scenario with a high interference among the D2D pairs, we propose a novel resource allocation consisting of two phases. In an initial phase, all available channels are assigned by the Hungarian algorithm so that each channel is occupied by just one D2D pair. In a reuse phase, multiple D2D pairs are sequentially added to the individual channels according to their priority expressed by channel quality and received interference from already added D2D pairs. The proposal significantly outperforms existing solutions and reaches close to theoretical upper bound capacity despite a very low complexity of the proposed algorithm.
Two-Phase Random Access Procedure for LTE-A Networks
Simultaneous random access attempts from massive machine-type communications (mMTC) devices may severely congest a shared physical random access channel (PRACH) in mobile networks. This paper presents a novel two-phase random access (TPRA) procedure to deal with the congestion caused by mMTC devices accessing the PRACH. During the first phase, the TPRA splits the mMTC devices into smaller groups according to a preamble selected randomly by the devices. Then, in the second phase, each group of devices is assigned with a dedicated channel to complete the random access procedure. The proposed concept allows a base station to adjust the number of dedicated channels in real-time according to the actual network load. We then present an analytical model to estimate the access success probability and the average access delay of the TPRA. Finally, we propose a simple formula to determine the optimal number of random access resources for the second phase of the proposed TPRA. Simulations are carried out to validate the analytical models and to demonstrate the benefits of the TPRA compared to competitive techniques.
Adaptive Hysteresis Margin Based on Fuzzy Logic for Handover in Mobile Networks With Dense Small Cells
To satisfy requirements on future mobile network, a large number of small cells should be deployed. In such scenario, mobility management becomes a critical issue in order to ensure seamless connectivity with a reasonable overhead. In this paper, we propose a fuzzy logic-based scheme exploiting a user velocity and a radio channel quality to adapt a hysteresis margin for handover decision in a self-optimizing manner. The objective of the proposed algorithm is to reduce a number of redundant handovers and a handover failure ratio while allowing the users to exploit benefits of the dense small cell deployment. Simulation results show that our proposed algorithm efficiently suppresses ping pong effect and keeps it at a negligible level (below 1%) in all investigated scenarios. Moreover, the handover failure ratio and the total number of handovers are notably reduced with respect to existing algorithms, especially in scenario with high number of small cells. In addition, the proposed scheme keeps the time spent by the users connected to the small cells at a similar level as the competitive algorithms. Thus, the benefits of the dense small cell deployment for the users are preserved.
Combined Shared and Dedicated Resource Allocation for D2D Communication
Device-to-device (D2D) communication is an effective technology enhancing spectral efficiency and network throughput of contemporary cellular networks. Typically, the users exploiting D2D reuse the same radio resources as common cellular users (CUEs) that communicate through a base station. This mode is known as shared mode. Another option is to dedicate specific amount of resources exclusively for the D2D users in so- called a dedicated mode. In this paper, we propose novel combined share/dedicated resource allocation scheme enabling the D2D users to utilize the radio resources in both modes simultaneously. To that end, we propose a graph theory-based framework for efficient resource allocation. Within this framework, neighborhood relations between the cellular users and the D2D users and between the individual D2D users are derived to form graphs. Then, the graphs are decomposed into subgraphs to identify resources, which can be reused by other users so that capacity of the D2D users is maximized. The results show that the sum D2D capacity is increased from 1.67 and 2.5 times (depending on a density of D2D users) when compared to schemes selecting only between shared or dedicated modes.
Conventional overlay and underlay spectrum sharing strategies enable the cognitive Small Cells (SCeNBs) to access a spectrum of macrocells. The problem of the overlay approach is strong dependency of its efficiency on an activity of macrocell users. Thus, not enough resources remain for the SCeNB users if the macrocell is loaded heavily. The main weakness of the underlay approach is that it can result in a low transmission efficiency because the transmission power level of the SCeNBs is restricted. To overcome the abovementioned problems of both spectrum sharing strategies, a hybrid spectrum sharing combining both overlay and underlay has been introduced in literature. In this paper, we propose a new distributed resource allocation algorithm for hybrid spectrum sharing tailored for realistic scenarios considering varying channel quality over individual resource blocks. The algorithm considers the buffer state at the SCeNBs, ratio of the resources available in the overlay and underlay modes, and channel quality experienced by the users at individual resource blocks. The proposed scheme increases the amount of traffic served for SCeNB users by 22.7% and reduces the packet delay by 27.1% for heavy loaded network comparing to existing schemes.
Modeling of Distributed Queueing-based Random Access for Machine Type Communications in Mobile Networks
Machine type communications (MTC) devices stay in idle mode to save energy and should perform random access (RA) procedure to obtain radio resources for data transmission. The RA procedure introduces access delay and extra power consumption for the MTC devices. Thus, RA needs to be optimized. In this letter, we develop low complexity analytical models to rapidly estimate maximum access delay and average number of preamble transmissions for distributed queueing-based random access (DQRA) protocol, which improves the performance of standard RA for MTC in LTE. The proposed model can be used to analyze the performance of group paging using DQRA. The performance analysis shows that the proposed analytical models accurately match the simulation results.
Selection between Radio Frequency and Visible Light Communication Bands for D2D
Device to device (D2D) communication is designed to accommodate high data rates required in future mobile networks. To maximize spectral efficiency of the communication, D2D pairs should reuse the same set of frequencies. However, this results in a strong interference among close D2D pairs or, in the worst case, even in outage. Both the interference and the outage can be reduced if highly interfered and highly interfering D2D pairs exploit a visible light communication (VLC) band instead of a common radio frequency (RF) band. This concept is known as RF-VLC D2D. In this paper, we target the problem of a selection between RF and VLC bands for each D2D pair in a multi user scenario. We define the RF and VLC selection as a multiobjective optimization problem targeting to minimize the outage and to maximize the system capacity. To solve this problem, we propose a low-complexity heuristic centralized algorithm choosing either RF or VLC for individual D2D pairs according to both the interference imposed by the D2D transmitters and the interference observed by the D2D receivers. For interpretation of the mutual interferences among the D2D pairs, we adopt graph theory. The simulations show that the proposed algorithm outperforms state of the art algorithms in both outage and capacity. Despite a very low complexity, the proposed algorithm reaches the performance close to the optimum.
Self-tuning Handover Algorithm Based on Fuzzy Logic in Mobile Networks with Dense Small Cells
Cellular networks are undergoing a major shift in their deployment and optimization. New infrastructure elements, such as small base stations, are being massively deployed, thus making future 5G cellular systems and networks heterogeneous. In order to operate successfully in a dense deployment, the small cells should have efficient self-organizing capabilities to intelligently adapt themselves to the neighborhood. In this paper, we introduce a novel handover algorithm targeting to reduce an amount of ping pong handovers and a handover failure ratio. The novel handover integrates a channel quality and UE’s velocity into a derivation of a new fuzzy logic-based threshold that is exploited for handover decision. Simulation results show that the proposed algorithm efficiently suppresses ping pong effect comparing to competitive algorithms and keeps it at negligible level (below 1%). At the same time, handover failure ratio is also reduced comparing to the competitive algorithms.
Vehicular Network-Aware Route Selection Considering Communication Requirements of Users for ITS
Increasing demands of mobile users on communication and new types of devices, such as sensors, machines, and vehicles, impose high load on cellular networks. Since requirements are expected to rise in a near future, new ways for cellular network offloading are needed. A promising solution for vehicles and vehicular users is to offload data to vehicular networks. To maximize offloading of the cellular networks, the vehicles can be navigated through areas characterized with more available communication capacity. Hence, we propose a novel scalable traveling route selection algorithm determining the route according to a traveling time and available throughput of both cellular and vehicular networks. While the maximum tolerated traveling time is defined by the vehicular users, an estimation of available throughput is based on a vehicular movement prediction. The proposed route selection algorithm is able to offload cellular network by up to 17% and time spent without required quality of connection can be reduced by 65%. At the same time, the traveling time is prolonged only negligibly in comparison with state-of-the-art algorithms.
Combination of Visible Light and Radio Frequency Bands for Device-to-Device Communication
The 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2017). Piscataway, NJ: IEEE, 2017. p. 1-7. 27. ISSN 2166-9589. ISBN 978-1-5386-3531-5.
Future mobile networks are supposed to serve high data rates to users. To accommodate the high data rates, a direct communication between nearby mobile terminals (MTs) can be exploited.
This type of communication in mobile networks is known as Device-to-device (D2D).
Furthermore, a communication in high frequency bands, such as, visible light communication (VLC), is also foreseen as an enabler for the high data rates.
In a conventional D2D communication, pairs of the communicating MTs should reuse the same frequencies to maximize spectral efficiency of the system.
However, this implies either interference among the D2D pairs or a need for complex resource allocation algorithms.
In this paper, we introduce a new concept for D2D communication combining VLC and RF technologies in order to maximize capacity of the system.
The objective of this paper is to analyze operational limits of the proposed concept and to assess potential capacity gains to give motivation for future research in this area.
Thus, we also discuss several practical issues related to the proposed RF--VLC D2D concept and outline major research challenges.
The performance analysis carried out in this paper shows that the RF--VLC D2D is able to improve the capacity in an indoor scenario by a factor of 4.1 and 1.5 when compared to standalone RF D2D and VLC D2D, respectively.
Distributed Architecture of 5G Mobile Networks for Efficient Computation Management in Mobile Edge Computing
Mobile cloud computing is a solution for offloading computation from mobile devices in order to overcome their major limitations: short battery life-time and limited computational power. However, the conventional centralized cloud with large server farms may result in a high delay of offloaded data transmission from the mobile device to the cloud and in congestion of backhaul due to offloading of high amount of data. These problems scale down the usage of common mobile cloud computing, especially for real-time services and applications. The perceived delay can be minimized by redeployment of computation resources to the edge of mobile network, for example, to the cloud-enabled base stations, as these are close to the users. To implement this new paradigm, the architecture of conventional mobile network must be enhanced with a computation management unit, which controls processing of offloaded tasks at cloud-enabled base stations. In order to reduce signaling delay and to minimize signaling load introduced by this concept, we introduce two options of new distributed deployments of the management unit. We also discuss integration of the proposed solution into 5G mobile networks based on C-RAN. By analysis and simulations of the proposed architectures, we proof that both signaling delay and signaling load could be significantly reduced comparing to centralized solutions.
Energy-Aware Dynamic Selection of Overlay and Underlay Spectrum Sharing for Cognitive Small Cells
Small cell (SC) base stations with cognitive capabilities are seen as an efficient way to cope with interference between the SC base stations and macrocell base stations (MBSs). Cognitive SCs may access the spectrum by means of overlay or underlay mode. An efficiency of the overlay mode fully depends on the activity of macrocell users (MUEs), since insufficient resources remain for small cell users (SUEs). Contrarily, the main weakness of the underlay mode is that it can result in low transmission efficiency due to restricted transmission power. Apart from the transmission efficiency of both modes, an energy consumption of the SCs should not be disregarded. Thus, we propose a centralized scheme selecting the spectrum sharing mode in the downlink, according to both the SUEs' throughput and the energy consumption of the SCs. The objective is to maximize the overall performance of the SCs, while their energy consumption is taken into account. Then, we also propose a distributed algorithm in order to decrease complexity and signaling overhead of the centralized scheme. The results show that the proposed dynamic selection significantly outperforms all competitive schemes in terms of the SUEs' throughput, while the throughput of MUEs is intact, and only negligible signaling overhead is generated in the case of the proposed distributed algorithm. Moreover, the proposed algorithm is able to notably decrease the energy consumption of the SCs.
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.
Performance of Mobile Networks with UAVs: Can Flying Base Stations Substitute Ultra-Dense Small Cells?
A crucial challenge for future mobile networks is to enable wide range of scenarios and use cases for different devices spanning from simple sensors to advanced machines or users’ devices. Such requirements call for highly flexible and scalable radio access network (RAN). To provide high flexibility and scalability in dynamic scenarios, flying base stations (FlyBSs), i.e., base stations mounted on general unmanned aerial vehicles, can be integrated into RAN. In this paper, implementation and operational issues related to the FlyBSs are discussed. Additionally scenarios where the FlyBS can be profitable are outlined. Furthermore, we define the architecture of a flying RAN (FlyRAN) encompassing the FlyBSs and enabling real-time control of whole RAN so that it can dynamically adapt to users’ movement and changes in their communication activity. Our results show the superior efficiency of FlyBSs comparing to an ultra-dense deployment of static base stations (BSs) for a realistic scenario with moving users. Our simulations suggest that one FlyBS can provide throughput comparable to static BSs deployed with density corresponding to inter-site distance of 45 meters. At the same time, energy efficiency of the communication for the user equipment can be improved more than 5-times. This indicates that integration of the FlyBSs into mobile networks can be an efficient alternative to ultra-dense small cell deployment, especially in scenarios with users moving in crowds.
Cloud-aware power control for real-time application offloading in mobile edge computing
Running computationally demanding real-time applications at the mobile user equipment (UE) is complicated because of limited battery life time of the UEs. One solution is to offload demanding computing tasks to a centralized cloud. Nevertheless, this option introduces significant delay consisting in delivery of the offloaded tasks to the cloud and back. Such delay is inconvenient for real-time applications. To cope with high delay, a concept of mobile edge computing has been introduced. The feasible way enabling mobile edge computing is to enhance the small cell base stations (SCeNBs) with computing capabilities. However, a high density of the SCeNBs together with even low mobility of users can result in often outage situation or handover when delay sensitive data cannot be delivered from the computing SCeNBs in time and become irrelevant. In this paper, we propose distributed cloud-aware power control algorithm, which targets to increase the ratio of delivery of computation results to the UE within required delay. Then, we enhance cloud-aware power control by adaptive algorithm. This algorithm exploits iterative process to find appropriate time when the power control is triggered in order to further improve the performance of the cloud-aware power control. The simulations demonstrate notable increase in the ratio of delivered offloaded tasks from the computing SCeNBs comparing with competitive schemes. At the same time, the amount of served data to the users exploiting common, non-cloud, services is increased. As the proposed solution is distributed, related signaling overhead is negligible. Copyright (c) 2015 John Wiley & Sons, Ltd.
Distance-Based Neighborhood Scanning for Handover Purposes in Network with Small Cells
Deployment of small cells into existing mobile networks can improve throughput and users’ quality of service. However, the new tier composed of small cells raises problems related to management of user mobility. Moving users must be able to discover cells in their neighborhood. For this purpose, the users perform neighborhood scanning. The scanning process should be frequent enough to avoid situations where the user is not aware of a close cell that has not been scanned. However, the frequent scanning of a high number of neighboring cells leads to wasting battery power for the user equipment and reducing the throughput of users. On the contrary, rare scanning can lead to a situation where a small cell is missing in the list of scanned cells, and thus, handover is not performed. This results in underutilization of the small cells and consequent overloading of macrocells. In this paper, we propose an efficient scanning algorithm suitable for future mobile networks. The objective of the proposed scheme is to maximize utilization of the small cells and to minimize energy consumption due to scanning. The proposal
exploits graph theory to represent a principle of obstructed paths in combination with knowledge of the previously visited cell and the estimated distance between cells. As the results presented in this paper show, our algorithm reduces energy consumption due to scanning and enables higher exploitation of small cells by
Distributed Hybrid Spectrum Access for Cognitive Femtocells in 5G Mobile Networks
The femto access points (FAPs) with cognitive capabilities, also known as cognitive FAPs, are able to efficiently mitigate interference in two-tier heterogeneous networks. Hence, the concept of cognitive FAPs can be seen as one of the key enablers for future 5G networks, where high density of FAPs is foreseen. However, conventional overlay and underlay spectrum sharing strategies enabling the cognitive FAPs to access spectrum of macrocells have several drawbacks. The main disadvantage of the former one is that its efficiency fully depends on the activity of macrocell users and insufficient resources can remain for the users of the FAP. The main weakness of the latter one is that it can result in low transmission efficiency because transmission power level of the FAPs is restricted. In this paper, we propose a novel hybrid spectrum sharing that allows the FAPs to use both overlay and underlay strategies simultaneously and, thus, increase performance of FAPs' users. The proposed scheme is fully distributed since the FAPs allocate resources autonomously. The results show that the proposed algorithm is able to significantly outperform competitive schemes in terms of served traffic for femtocell users and, simultaneously, served traffic for macrocell users is intact.
Dynamic Resource Allocation Exploiting Mobility Prediction in Mobile Edge Computing
In 5G mobile networks, computing and communication converge into a single concept. This convergence leads to introduction of Mobile Edge Computing, where computing resources are distributed at the edge of mobile network, i.e., in base stations. This approach significantly reduces delay for computation of tasks offloaded from users' devices to cloud and reduces load of backhaul. However, due to users' mobility, optimal allocation of the computational resources at the base stations might change over time. The computational resources are allocated in a form of Virtual Machines (VM), which emulate a given computer system. User's mobility can be solved by VM migration, i.e., transfer of VM from one base station to another. Another option is to find a new communication path for exchange of data between the VM and the user. In this paper we propose an algorithm enabling flexible selection of communication path together with VM placement. To handle dynamicity of the system, we exploit prediction of users' movement. The prediction is used for dynamic VM placement and to find the most suitable communication path according to expected users' movement. Comparing to state of the art approaches, the proposal leads to reduction of the task offloading delay between 10% and 66% while energy consumed by user's equipment is kept at similar level. The proposed algorithm also enables higher arrival rate of the offloading requirements.
Path selection enabling user mobility and efficient distribution of data for computation at the edge of mobile network
Convergence of mobile networks and cloud computing enables to offload heavy computation from a user equipment (UE) to the cloud. The offloading can reduce energy consumption of the UEs. Nevertheless, delivery of data to a centralized cloud leads to high latency and to overloading backhaul network. To overcome these constrains, computing capabilities can be brought closer to the user and integrated into small cell base stations deployed in mobile networks. This concept of cloud-enabled small cells is known as small cell cloud (SCC). In the SCC, the UEs benefit from proximity to the computing stations resulting in both lower latency and alleviating load of backhaul. In this paper, we propose a path selection algorithm finding the most suitable way for data delivery between the mobile UE and the cells performing computation for this particular UE. The path selection algorithm estimates transmission delay and energy consumed by the transmission of offloaded data and selects the most suitable base station for radio communication accordingly. The path selection problem is formulated as Markov Decision Process (MDP). The algorithm is suitable for parallel computation in dynamic scenarios with mobile users and handles mobility for users exploiting computing services in the SCC. Comparing to conventional approach for delivery of data to computing cells, the proposed algorithm reduces the delay up to 54.3% and UE's energy consumption is decreased by up to 7.5%. Moreover, users’ satisfaction with data transmission delay is increased by up to 28% and load of small cell's backhaul is lowered by up to 29%.
Performance evaluation of computation offloading from mobile device to the edge of mobile network
Small Cell Cloud (SCC) consists of Cloud-enabled Small Cells (CeSCs), which serve as radio end-points for mobile user equipments (UEs) and host computation offloaded from mobile UEs. SCC hereby brings advantages of a centralized cloud computation to the users' vicinity. The SCC architecture provides a mechanism for distribution of computation demand across the CeSCs. An effectiveness of the offloading is determined based on quality of radio channel between the UEs and the CeSC and predicted computation complexity. In this paper, we introduce an implementation of an offloading framework to facilitate adaptation of mobile apps for the SCC and to handle low-level communication between the app and the SCC. An evaluation of the offloading framework is conducted using Augmented Reality (AR) app, which requires intensive computations and low latency. The offloading framework and the AR app are a basement for the SCC testbed used to proof the concept of the computation offloading. Various computation and radio parameters are investigated to reveal benefits of the SCC. According to the performed measurements, the computation offloading can decrease latency up to 88 % and energy consumption of the UEs up to 93 %.
A Seamless Integration of Computationally-Enhanced Base Stations into Mobile Networks Towards 5G
Following Mobile Cloud Computing, Mobile Edge Computing and Network Functions Virtualization tendencies, we envisage the utilization of computationally-enhanced base stations as computing nodes in which Virtual Machines can be deployed to perform computing tasks, leveraging the closeness of computing resources to end-users. This paper presents a seamless approach for the deployment of computationally-enhanced Small-Cells, also applicable to macro base stations, with no impact on the LTE-A architecture. To that end, the conventional mobile traffic and the traffic generated and consumed by the new computing resources are segregated and handled independently at the access point, with the latter being transmitted through the radio channel making use of the pre-established Data Radio Bearers. Assuming a general-purpose hardware configuration for the Small-Cells, we describe the functionality of the different physical and logical components along with the new protocol stacks and interfaces. Finally, we evaluate the additional delay and amount of signaling overhead introduced by the system to benchmark the proposed solution.
Cross-layer approach enabling communication of high number of devices in 5G mobile networks
Introduction of Internet of Things and Machine Type Communication to future mobile networks will cause significant increase in the number of connected devices. At the same time, the connected devices can change traffic patterns as frequent transmission of small volumes of data is expected from sensors and machines. Transmission of such data is very inefficient due to redundancy of signaling information. In this paper, we analyze limits for the number of devices and machines communicating in current 4G mobile network. Then, we propose a novel solution, which shifts the current limits of the number of communicating devices towards requirements on 5G mobile networks. The proposed solution exploits cross-layer approach considering buffering of data and clustering of nearby users in order to minimize overhead and improve transmission efficiency. This way, we can increase the number of devices served by a single cell up to 24 times comparing to the state of the art solution.
Distributed Hybrid Spectrum Sharing for OFDMA-based Cognitive Femtocells in 5G Networks
The femto access points (FAPs) with cognitive capabilities are seen as an efficient way to cope with interference between the FAPs and the macrocells (MBSs). This interference should be properly addressed in emerging 5G mobile networks to enable dense deployment of the FAPs. The cognitive FAPs may access the spectrum by means of overlay or underlay mode. The main drawback of the overlay mode is that its efficiency fully depends on the activity of the macrocell users (MUEs) and insufficient resources can remain for the FAPs' users (FUEs). The main drawback of the underlay mode is that it can result in low transmission efficiency due to low power level. In this paper, we propose a distributed hybrid spectrum sharing that dynamically selects the mode currently more beneficial in terms of FUEs' throughput. The results show that the proposed scheme is able to significantly outperform both overlay and underlay modes in terms of FUEs' throughput while the performance of MUEs is intact.
Enhancement of Hybrid Cognitive Approach for Femtocells
The use of femtocells with cognitive capabilities is considered as a promising way for interference mitigation. The femto access point (FAP) accesses the spectrum either in overlay or underlay fashion. In the former case, the FAPs utilize only radio resources currently not occupied by the macrocell. In the latter case, the whole bandwidth may be used but power of the FAPs is restricted. Both spectrum sharing approaches can be coupled to make the protection of primary users (PUs) more efficient. The merging of both ways results in a combined spectrum sharing (CSS). Additional combination of underlay approach and the CSS is known as a hybrid cognitive approach (HCA). In the HCA, the FAPs access the spectrum of the primary cellular operator through the underlay approach while the bandwidth of secondary cellular operators can be accessed by means of the CSS. In this paper, we propose an enhanced hybrid cognitive approach (EHCA), which main objective is to decrease sensing overhead and increase performance of the femtocell users while macrocell users are only minimally negatively affected. The enhancement consists in extension of power control mechanism for femtocells. The simulation results indicate that the sensing overhead can be decreased by the EHCA up to 48% and femtocell users throughput increased by up to 13.5% while the performance of the macrocell users is degraded only negligibly (less than 1.3%) when compared to former HCA scheme.
In-Band Device-to-Device Communication in OFDMA Cellular Networks: A Survey and Challenges
Direct communication between two or more devices without the intervention of a base station, known as device-to-device (D2D) communication, is a promising way to improve performance of cellular networks in terms of spectral and energy efficiency. The D2D communication paradigm has been largely exploited in non-cellular technologies such as Bluetooth or Wi-Fi but it has not yet been fully incorporated into existing cellular networks. In this regard, a new proposal focusing on the integration of D2D communication into LTE-A has been recently approved by the 3GPP standardization community as discussed in this paper. In cellular networks, D2D communication introduces several critical issues, such as interference management and decisions on whether devices should communicate directly or not. In this survey, we provide a thorough overview of the state of the art focusing on D2D communication, especially within 3GPP LTE/LTE-A. First, we provide in-depth classification of papers looking at D2D from several perspectives. Then, papers addressing all major problems and areas related to D2D are presented and approaches proposed in the papers are compared according to selected criteria. On the basis of the surveyed papers, we highlight areas not satisfactorily addressed so far and outline major challenges for future work regarding efficient integration of D2D in cellular networks.
Offloading Multiple Mobile Data Contents Through Opportunistic Device-to-Device Communications
Opportunistic device-to-device (D2D) communication is the approach proposed to offload mobile data traffic for cellular networks. In opportunistic D2D communication, the network has to appoint relaying users to distribute content(s) to normal subscribers under a given delay-tolerance threshold. In general, the total number of relaying users is fixed. Identifying proper number of relaying users is one of the key challenges in opportunistic D2D communication. The network has to select proper number of relaying users for each content to minimize the amount of mobile data traffic. This paper presents a popularity-based relaying user selection algorithm to determine the number of relaying users for distributing multiple contents with different popularity. An analytical model is then presented to estimate the amount of reduced mobile data traffic under single-hop and multi-hop opportunistic forwarding scenarios. Results obtained by simulations as well as by our proposed analytical model show that the proposed popularity-based algorithm can find the total number of relaying users to the amount of reduced mobile data traffic. For services which have longer delay-tolerance threshold, the proposed popularity-based algorithm requires less relaying users and can achieve similar amount of reduced mobile data traffic as the state-of-the-art random fully-allocation algorithm does. For services which have shorter delay-tolerance threshold, the proposed popularity-based algorithm provide significant gain comparing with the random fully-allocation algorithm.
Self-optimizing Neighbor Cell List with Dynamic Threshold for Handover Purposes in Networks with Small Cells
To select a proper target cell for handover of mobile users, signal level of cells in user's neighborhood is scanned by a user equipment (UE). Cells assumed to be scanned are included in the so-called neighbor cell list (NCL). Conventionally, the NCL is managed according to the probability of handover of users to a target cell with fixed threshold. Nevertheless, the size of NCL could be significant if this approach is applied to networks with small cells. In this paper, we exploit knowledge of handover probability among cells derived from a handover history to reduce the amount of scanned cells. We introduce dynamic adaptation of the amount of cells to be scanned according to the quality of signal of a serving cell, measured by the UE. We also investigate impact of relation between the handover probability and the signal level to maximize efficiency of this approach. Further, the NCL management considering either summarized handover history of all UE or individual history of each user is compared in our evaluations. As the results show, both methods notably reduce the amount of cells to be scanned, while call drop rate and outage of the users are still negligible as in the conventional way.
An Architecture for Mobile Computation Offloading on Cloud-enabled LTE Small Cells
Small cell networks are currently seen as a new way to satisfy the increasing wireless traffic demand. The proximity of base stations to subscribers brings many possibilities for the development of new applications, including new offerings based on cloud computing. Smartphones can directly offload applications to close base stations, provided that these are equipped with additional computational and storage resources. The cloud concept applied in the framework of small cells can also combine radio and computation aspects in order to optimise the service delivery. This paper introduces a new element called the Small Cell Manager (SCM), which optimises the overall operation of a cluster of cloud-enabled small cells. The SCM, aware of the cluster situation in terms of both radio and cloud aspects, interacts not only with the cloud-enabled base stations, but also with LTE core network components. To that end, different possibilities for the general architecture of a small-cell-cloud are analysed. Furthermore, the paper describes different evaluation criteria an LTE operator has to consider before adopting this approach in order to optimise the required investment and maximise benefits.
Centralized Dynamic Resource Allocation Scheme for Femtocells Exploiting Graph Theory Approach
This paper focuses on mitigation of cross-tier and co-tier interference for dense deployment of the femtocells (FAPs). We propose a centralized dynamic radio resource allocation scheme exploiting graph theory approach. The FAPs either utilize an overlapping allocation mode (OAM) or a non-overlapping allocation mode (NAM). The allocation mode is dynamically selected by a control unit (CU) depending on the changing interference pattern among individual FAPs. The FAPs are assumed to be mutually interfered if interference is higher than a specified threshold. In order to create interference matrix among the FAPs, we use Bron-Kerbosch algorithm. In case the FAPs are assessed to be interfered, the CU also allocates resources in the NAM mode in dynamic nature in dependence on current traffic load of the FAPs. The results indicate that the proposal offers significantly higher throughput for the macro users than other competitive schemes. Simultaneously, femto users perform satisfactorily as well.
Cloud-aware Power Control for Cloud-enabled Small Cells
Requirements of current services and applications on computational power are still increasing. Hence, the utilization of these by the mobile user equipment (UE) having limited battery lifetime is quite a challenge. To cope with this problem, the mobile cloud computing can be exploited to offload high complex applications to the cloud. Nevertheless, the conventional mobile cloud solution introduces high delay and limits the use of delay sensitive services and applications. One feasible solution is to enhance small cells (SCeNBs) with computing capabilities and, thus, perform computing closer to the users accomplishing low delay. However, the high density deployment of the SCeNBs and UEs’ mobility can result in often outage situation or handover when low delay tolerant data cannot be delivered from the cloud
on time and become irrelevant. In this paper we propose power control algorithm, which purpose is to guarantee that the requests processed by the small cell cloud are received by the UE within required delay. This is done by coarse and dynamic fine setting of the SCeNB's transmitting power level. The simulations demonstrate the amount of undelivered requests from the small cell cloud can be significantly minimized when compared to competitive schemes. At the same time, QoS of non-cloud users is only slightly impaired when compared to the scheme giving solely preferences to non-cloud users. We also show that the overhead introduced by our power control mechanism is negligible.
Hybrid Cognitive Approach for Femtocell Interference Mitigation
In this paper, we introduce new concept for femtocells with the purpose to minimize cross-tier interference to the macrocell users (MUEs). The cross-tier interference is mitigated by the power control algorithm minimizing the power level and, thus, ensuring that all currently active femto users (FUEs) can attach to the femto access points (FAPs). To guarantee quality of service to the FUEs even at heavy load, despite low transmitting power, the FAPs can opportunistically utilize also SU bands. To that end, we denote our scheme as a hybrid cognitive approach, which is distinguished by the fact that the FAPs can access bandwidth as a primary users (PU) and secondary users (SU) at the same time. In order to generate less overhead introduced by sensing for the purpose of determining available spectrum, we also propose an algorithm that adaptively changes a sensing period. The results show that our proposal is able to significantly reduce cross-tier interference nearly to the same level as achieved by algorithms focusing on maximization of the MUEs' performance. At the same time, our proposal ensures that the performance of the FUEs is kept at satisfactory level similarly as in the case of the algorithms focusing solely on the performance of the FUEs.
In future 5G networks, in-band interference is perceived as one of the most critical performance bottlenecks. While current solutions classically treat interference as an additional source of noise, recent advances in information theory show that interference is not necessarily an opponent, but might be cancelled or supressed. In this paper, we propose a threefold optimization method, which couples reduced complexity interference classification and matching techniques, to enhance the system performance. The matching objective consists in i) defining coalitions of users assigned to each Access Points (AP); ii) match interferers that will be transmitting on the same spectral resources into groups of interferers; and iii) define the transmission rates and interference regimes for each user inside each group. Our analytical study and simulations results show that our proposed solution allows for system spectral efficiency enhancements, compared to classical reference scenarios.
Methodology and Tool for Energy Consumption Modeling of Mobile Devices
Current mobile devices evolve quickly and bring more powerful hardware to users. However, rate of such development surpasses advances in creation more durable batteries. Although energy awareness of mobile devices is utmost important, the significance of results of energy consumption modeling drops rapidly due to never-ending massive release of new mobile devices. In this paper, we describe new methodology and tool developed for measurement of energy consumption of user devices. Using this new tool, we carry out measurements of selected non-radio components (CPU, screen, I/O storage operations, speakers) of smartphones. For each component, several parameters are investigated. These parameters influence how a component is used by a user and therefore have great impact on overall energy consumption. Moreover, mathematical models are proposed in order to predict energy consumption without necessity to perform measurements and to determine expected consumption in real time.
Path Selection Using Handover in Mobile Networks with Cloud-enabled Small Cells
IEEE 25th Annual International Symposium on Personal, Indoor and Mobile Radio Communications. Piscataway: IEEE Conference Publications, 2014. p. 1480-1485. ISSN 2166-9589. ISBN 978-1-4799-4912-0.
To overcome latency constrain of common mobile cloud computing, computing capabilities can be integrated into a base station in mobile networks. This exploitation of convergence of mobile networks and cloud computing enables to take advantage of proximity between a user equipment (UE) and its serving station to lower latency and to avoid backhaul overloading due to cloud computing services. This concept of cloud-enabled small cells is known as small cell cloud (SCC). In this paper, we propose algorithm for selection of path between the UE and the cell, which performs computing for this particular UE. As a path selection metrics we consider transmission delay and energy consumed for transmission of offloaded data. The path selection considering both metrics is formulated as Markov Decision Process. Comparing to a conventional delivery of data to the computing small cells, the proposed algorithm enables to reduce the delay by 9% and to increase users' satisfaction with experienced delay by 6.5%.
Prediction of Channel Quality after Handover for Mobility Management in 5G
The fifth generation of wireless networks should enable the same experience to users at home, in the office or on the move thanks to seamless handover. Call admission control (CAC) provides the means to avoid call drops due to lack of resources at a target cell during handover. The purpose of the CAC is to decide if handover should be initiated or if a new call can be established. A specific quantity of resources is reserved to the users entering the cell in the future to avoid call drops. A prediction of user's movement and amount of resources required by the users after handover can be performed in order to optimize amount of reserved resources. In this paper, we address prediction of the number of resources required by the users at the target cell after handover. To that end, we propose new approach for prediction of channel quality indicator (CQI) after handover. The prediction exploits knowledge of handover hysteresis and decomposition of interference into two parts. As the results show, the proposed algorithm increases ratio of successfully predicted CQI up to 1.9 times with respect to existing approaches.
Q-Learning-based Prediction of Channel Quality after Handover in Mobile Networks
IEEE 25th Annual International Symposium on Personal, Indoor and Mobile Radio Communications. Piscataway: IEEE Conference Publications, 2014. pp. 1359-1364. ISSN 2166-9589. ISBN 978-1-4799-4912-0.
To avoid call drops after handover due to unavailability of radio resources at a target handover cell, call admission control procedure reserves a specific amount of resources for users performing handover to this cell. If a high amount of resources is reserved, the available capacity for users served by the cell is lowered. Contrary, if a low amount of resources is booked for users entering the new cell, handover cannot be performed and user's connection is dropped. To optimize the amount of reserved resources, we propose an algorithm for prediction of channel quality between the user and the target cell after completing handover to the target cell. The algorithm is based on the knowledge of handover hysteresis and on decomposition of overall interference caused by other cells in the network. The prediction accuracy is tuned by correction parameter, which is dynamically set based on Q-learning approach. As the results show, the proposed algorithm with learning improves the efficiency of channel quality prediction up to twice comparing to conventional solution.
QoS-ensuring Distribution of Computation Load among Cloud-enabled Small Cells
With increasing requirements of mobile users on computational demanding applications, the need for offloading of computation to a cloud is a convenient way to provide high QoS and to lower energy consumption of a User Equipment (UE). However, common centralized cloud can prolong overall application processing time due to high delay of data delivery from the UE to the cloud and back. To reduce data delivery delay, a concept known as Small Cell Cloud (SCC) can be adopted. The SCC is based on equipping small cell base stations by additional computing capacity and enabling distributed computation for mobile cloud application. For proper function of the SCC, offloaded computing tasks must be efficiently and uniformly distributed among all computing cells. In this paper, we design the algorithm for selection of the computing cells that increases user’s satisfaction with experienced delay due to data transmission and computing. The algorithm selects the
computing cell based on combination of users’ requirements and the SCC status. As the results show, the proposed algorithm is able to provide higher satisfaction comparing to competitive approaches for all types of backhauls while the balancing of load is not significantly affected.
Radio Resource Sharing Among Users in Hybrid Access Femtocells
A problem related to deployment of femtocells is how to manage access of users to radio resources. On one hand, all resources of the femtocell can be reserved for users belonging to a closed subscriber group (CSG), which is a set of users defined by a femtocell subscriber. This approach, known as closed access, however, increases interference to users not included in the CSG as those users do not have a permission to access this femtocell. Contrary, resources can be shared by all users with no priority in an open access mode. In this case, the femtocell subscriber shares radio as well as backhaul resources with all other users. Thus, throughput and quality of service of the subscriber and the CSG users can be deteriorated. To satisfy both the CSG as well as non-CSG users, a hybrid access is seen as a compromise. In this paper, we propose a new approach for sharing radio resources among all users. As in common cases, the CSG users have a priority for usage of a part of resources while rest of the resources is shared by all users proportionally to their requirements. As the simulation results show, the proposed resource sharing scheme significantly improves throughput of the CSG users and their satisfaction with granted bitrates. At the same time, throughput and satisfaction of the non-CSG users is still guaranteed roughly at the same level as if conventional sharing schemes are applied.
Vertical Handover Decision in Heterogeneous Wireless Networks with Femtocells
The implementation of small base stations, known as femtocells, can significantly improve network performance. Nevertheless, the inherent problem of the femtocells consists in significant amount of initiated handovers that could decrease a quality of service. This issue is further emphasized if the femtocells and the macrocells utilize different access technology, as vertical handover between them introduces longer interruption in the communication. Still, performing vertical handover can be profitable if other technology can offer higher quality of service. This paper contemplates three vertical handover decision strategies formerly considered for networks without femtocells and analyzes their performance if the femtocells are deployed. Since these strategies either introduce long interruption duration, high degradation of quality of service, or both, we also propose new vertical handover strategy considering femtocells features, such as small coverage and their vast deployment. Analytical evaluations and simulation results indicate that the proposed strategy can guarantee the highest quality of service for all considered performance metrics.
Dynamic Optimization of Neighbor Cell List for Femtocells
To select appropriate target cell for handover if a user is moving, cells in user's neighborhood must be scanned and their signal quality must be measured by a User Equipment (UE). The cells intended to be scanned are listed in a Neighbor Cell List (NCL). The NCL is defined for each cell in the network and it is distributed to the users. A size of the NCL can be negatively influenced by dense deployment of cells with small radius, such as femtocells. In this paper, we investigate potential reduction of an amount of cells in the NCL to minimize signaling overhead and time required for scanning in networks with femtocells. Contrary to a conventional management of the NCL, we reduce the NCL for each UE individually. The lower number of cells in the UEs' NCL is achieved by consideration of mobility patterns of individual user. To avoid situation when a real target cell is missing in the NCL, we propose a dynamic adaptation of the UE's NCL according to the quality of signal measured by the UE from a serving cell. As the results show, the proposed approach with dynamic threshold significantly reduces amount of scanned cells comparing to competitive algorithms. Simultaneously, the outage probability and call drop rates are still kept at minimum level by our proposal.
Handover of Relay Stations for Load Balancing in IEEE 802.16
The load balancing in wireless networks is a very effective way for maximization of a system throughput. The paper proposes a new load balancing scheme in order to avoid a congestion of base stations (BSs) in IEEE 802.16 standards. While in many technical studies the load balancing is achieved by a handover (HO) of mobile stations (MSs), the novelty of our approach lies in the utilization of the HO of relay stations (RSs). Hence, the algorithm enabling load balancing via RSs is developed and optimized. Furthermore, the paper contemplates the implementation of the proposed mechanism to networks based on IEEE 802.16 standards. The performance of the mechanism is evaluated in terms of achieved system throughput and signaling overhead both at the air interface and over the wired backbone. The obtained results indicate that the load balancing mechanism through the HO of RSs outperforms existing load balancing mechanisms exploiting conventional HO of MSs
Mitigation of redundant handovers to femtocells by estimation of throughput gain
A deployment of femtocells to mobile wireless networks can increase a throughput of indoor as well as outdoor users. On the other hand, it introduces several problems such as serious interference or high number of performed handovers. This paper is focused on mitigation of redundant handovers to femtocells using open or hybrid access. The redundant handovers decrease user’s throughput due to a management overhead and due to introduced interruption. We design a novel handover decision algorithm based on an estimation of throughput gain reached by a handover to a femtocell. In the proposal, the handover is initiated only if the estimated gain in user’s throughput exceeds a predefined threshold. As the results indicate, high ratio of eliminated redundant handovers is achieved by the designed procedure. Moreover, a drop in user’s throughput the handover procedure is reduced by the proposed algorithm and thus the user’s throughput is increased.
Optimization of association procedure in WiMAX networks with relay stations
When a MS enters to the WiMAX network, a network entry procedure has to be performed. The aim of procedure is twofold. Firstly, several connections between the MS and BS are created, i.e. basic, primary and secondary management connections to control data transmissions. Secondly, the MS is admitted into the network. According to the IEEE 802.16 standard, a MS always tries to associate to BS with the highest received signal quality. This method is suitable as long as the MS is directly connected to the network via BS. However by introducing relay stations to the WiMAX architecture, the MS entry procedure needs to be modified. Mainly, the point of attachment influences the network performance. This paper proposes an optimized association procedure which takes into account the use of relays stations in the network. The obtained results show improvement of system performance.
Optimization of SINR-based Neighbor Cell List for Networks with Small Cells
In this paper, we propose an optimization of Neighbor Cell List (NCL) management algorithm. The goal is to minimize a number of scanned cells for handover purposes while a call drop rate is not increased. To that end, the NCL is dynamically optimized according to the SINR observed by a User Equipment (UE) from its serving cell. If a UE is in the cell center, only the serving cell is scanned. Contrary, if the UE moves closer to the cell edge, also other cells are inserted to the list of scanned cells. The cells are included in the list based on the probability of handover to these cells. The optimization presented in this paper consists in derivation of the optimal value of the parameters that describes a relation between a handover probability threshold for scanning and SINR measured by the UE. First, we provide analytical analysis of the problem and then we confirm the derived optimal values by means of simulations. The results show the proposed optimization of the NCL management is able to reduce the number of scanned cells significantly while the call drops due to NCL can be eliminated.
Self-configured Neighbor Cell List of macro cells in network with Small Cells
The paper proposes an algorithm for automatic creation of the Neighbor Cell List with reduced number of included cells. The proposed algorithm exploits knowledge of the last visited cell in combination with the statistical information on performed handovers in the past to determine the possibility of transition to the neighboring cells.
Small cell cloud: Load balancing of computational resources allocated to users in a small cell cloud
In this article, we propose an algorithm for balancing the computational load among cells, taking into account the delay requirements imposed by users and the available computational resources of the cells. Analysis of performance of this novel method of load balancing confirms the efficiency of the algorithm, leading to increased satisfaction among users with the quality of service provided through the small cell cloud.
Accuracy of position determination: GLONASS as a support of GPS
Besides the most widely used American navigation and positioning system GPS, Russian system GLONASS is available. Although GLONASS is already in full operational capability, commercial devices use GLONASS signal only to refine the position formerly determinate by GPS. Therefore, we focus on comparison of an error in position estimation by standalone GPS and by GPS with support of GLONASS in this paper. Results show that using the combination of both systems significantly improves an accuracy of the positioning. The improvement in accuracy is also influenced by the type of environment (height of building) in neighborhood of the user position.
Connection Cost Based Handover Decision for Offloading Macrocells by Femtocells
Femtocells can offload macrocells and reduce a cost of transmitted data in wireless networks. If a connection via the femtocell is of a lower cost than via the macrocell, a time spent by users connected to the femtocells should be maximized. This leads to a reduction of the overall cost of user's connection. Besides, a prolongation of the time spent by users in the femtocells reduces load of the macrocells. Therefore, an extension of handover is presented in this paper. The extension consists in consideration of the connection cost together with user's requirements on a service quality. To that end, a conventional handover decision is modified to achieve higher efficiency in prolongation of the time spent by the users in the femtocells. As the results show, the user who does not require high quality of service spent more time connected to the femtocells and thus the macrocell can be offloaded.
Fast Cell Selection with Efficient Active Set Management in OFDMA Networks with Femtocells
In 4 G wireless networks, only hard handover is defined to support users' mobility. However, dense deployment of femtocells leads to significant rise in amount of initiated handovers. Thus, inevitable decrease in quality of service (QoS) experienced by users is observed. In this article, we investigate possible introduction of fast cell selection (FCS) to OFDMA-based network with femtocells. The goal is to minimize negative impact of user's mobility on user's QoS and to take an advantage of macro diversity. First, we define necessary enhancements in current LTE standards to facilitate implementation of FCS to OFDMA networks. Afterward, impact of FCS on network performance is evaluated. Second, we propose novel algorithm for active set management, which considers specifics of femtocells. As simulation results demonstrate, even FCS with conventional active set management is profitable for the networks performance. Nevertheless, the proposed innovation of active set management procedure further improves efficiency of FCS comparing to existing schemes. We also presents the most common content of active set observed from simulations if novel algorithm is exploited.
Implementation of small base stations, known as femtocells, increases the throughput of indoor as well as outdoor users. However it brings several issues that need to be addressed. This chapter is focused on mobility management and problems closely related to the handover procedure. The main challenge is to guarantee efficient handover from/to/between femtocells. It means to ensure minimum signaling overhead due to unnecessary handovers, to minimize handover interruption, and to mitigate interference caused by elimination of redundant handovers. The basic principle of the handover is explained together with the main challenges concerning the handover in a scenario with deployed femtocells. Furthermore, individual issues are described in detail and possible ways to solve them are contemplated. The chapter does not stick to any specific standard; however, it is focused on the general principles and problems of the handover procedure from the femtocell's point of view.
Handover with Consideration of Connection Cost in Femtocell Networks
One of the most important benefits of femtocells is possibility to offload macrocells. Therefore, an interest of operators is to prolong a time spent by users connected to the femtocells. However, the longer time in the femtocell can brings lower quality of service due to lower quality of communication channel. Hence, the operators should compensate the drop in
quality to users. A way of prolongation of time in femtocells is to initiate handover to the femtocell as soon as possible and postpone handover from the femtocell. This paper analyzes
impact of modified hysteresis on several handover decision strategies. For this purpose, the handover decision phase is adjusted to prolong the time spent by users in the femtocells. This
way, macrocells can be offloaded. The time spent in femtocells is prolonged according to customers' willingness to tolerate worse quality of connection in exchange for lower cost of connection provided by the femtocell.
On Efficiency of ARQ and HARQ Entities Interaction in WiMAX Networks
A technique based on either Automatic Repeat reQuest (ARQ) or Forward Error Correction (FEC) is conventionally used to repair erroneous data in wireless networks. The ARQ is backward mechanism that uses a feedback channel for the confirmation of error-free data delivery or to request a retransmission of corrupted data. This method can increase a network throughput if radio channel conditions are getting worse. On the other hand, the ARQ method increases the delay of packets due to the retransmission of former unsuccessfully received packets. The FEC can increase user's data throughput over the channel with poor quality despite the fact that additional redundant bits are coded together with users' data at the transmitter side. The method combining both above mentioned methods is called Hybrid ARQ (HARQ). All three error correction mechanisms are implemented on physical and/or Medium Access Control (MAC) layer.
Optimization of Adaptive Hysteresis for Elimination of Handovers in Networks with Small Cells
Dense deployment of cells with low radius, known as small cells, in wireless networks can notably increase an amount of initiated handovers. Consequently, the quality of service is lowered due to handover interruption and due to additional signalling overhead generated by redundant handovers. In this paper, we optimize a technique for elimination of redundant handovers denoted as an adaptive hysteresis. The adaptive hysteresis enhances conventional hysteresis by dynamic setting of current level of the hysteresis according to the relation of signal level received from serving and target cells. The improvement in performance is achieved by derivation of optimum values of parameters used in equation for determina-tion of the actual level of hysteresis. As the simulation results show, significant gain in throughput can be reached by the selection of the optimum values instead of former values de-fined for macrocells. Further, we investigate an impact of small cells density on the performance of the adaptive hysteresis. The performance improvement reached by the adaptive hysteresis is even more notable with increased density of small cells.
Performance of Fast Cell Selection in Two-Tier OFDMA Networks with Small Cells
If large amount of small cells, i.e., femtocells, picocells or microcells, is deployed, users perform handover among small cells and macrocells very often. In OFDMA networks, only hard handover is usually supported as this type can be implemented easily. On the other hand, frequent hard handover negatively influence a quality of service experienced by users. The degradation of the quality is a consequence of a short interruption in communication during hard handover and due to redundant overhead generated for controlling and managing the handover procedure. This paper investigates performance of a fast cell selection applied in two-tier networks with small cells and compares it with the hard handover. We focus on the impact of a backhaul capacity limitation on the performance of both hard handover and fast cell selection. As our results indicate, there is a gain in throughput introduced by the fast cell selection if the backhaul of the small cells is not a bottleneck in term of capacity. Nevertheless, if the backhaul capacity is limited, the fast cell selection is profitable only for low bitrates. This observation indicates a need for mandatory consideration of the backhaul capacity for the fast cell selection in the networks with small cells.
Adaptive Techniques for Elimination of Redundant Handovers in Femtocells
In VoIP, the voice is transmitted over the IP networks in the form of packets. This way of voice transmission is highly cost-effective since the communication circuits need not be permanently dedicated to one connection; however, the communication band is shared by several connections. On the other hand, the utilization of IP networks causes some drawbacks that can result in the lower Quality of Service. This chapter evaluates impact of several factors on VoIP speech quality.
The paper proposes novel routing method for two users connected to the same femtocell communicating with each other. In conventional case, data are sent in two sequential steps.
In the first step, data are transmitted from the user to the femtocell. In the second step, data have to be retransmitted to other user. We suggest transmitting user's data directly instead
of two hop communications if both users are sufficiently close to each other. In addition, we propose exchange of management messages in order to implement our routing scheme in LTE-A standard. The simulation results indicate that the throughput offered by femtocells can be significantly increased.
This paper proposes a solution for the handover procedure that enables reduction of the handover interruption time by utilization of a handover prediction. The prediction of handover allows performing a part of handover procedure in advance to the interruption of a connection caused by handover. The goal is to design the handover procedure that fulfills the requirements on the handover interruption according to the originating IEEE 802.16m standard for a wide range of frame durations.
NMEA protocol is used for communication between GPS chip and host device in navigation equipment. The software tool developed for analyzing of NMEA is introduced and presented in this paper. The interface of the developed software enables to monitor visible satellites, levels of signal received from satellites, monitor MNEA communication, and determination of user's location based on analysis of information received from satellites.
Impact of Femtocells Backbone Capacity Limitation on Performance of Power Control
13th International Conference on Research in Telecommunication Technologies 2011. Brno: VUT v Brně, Fakulta elektrotechniky a komunikačních technologií, 2011. pp. 61-65. ISBN 978-80-214-4283-2.
The paper is focused on power control mechanism for femtocells. The main purpose of the power control is to decrease femtocells' transmission power to minimize number of generated mobility events. The femtocells are connected to the Internet via wired connection such as DSL or optical fiber and this backbone connection can serve as a bottleneck to the femtocells. Thus the purpose of this paper is to investigate the impact of backbone limitation on performance of power control mechanism. The performance of power control is evaluated in terms of number of mobility events, throughput for outdoor users, and femtocells' transmitting power. The simulation results demonstrate that especially for narrow channel bandwidth allocated to the FAP, the performance of power control can be significantly improved if backbone capacity is limited.
Improvement of handover prediction in mobile WiMAX by using two thresholds
One of the most important challenges in mobile wireless networks is to provide full mobility together with minimum degradation of quality of service. This can be ensured by handover prediction. Handover prediction means a determination of the next station that will serve a mobile station. This paper proposes a prediction technique based on monitoring the signal quality between the mobile station and all base stations in its neighborhood. The proposed technique utilizes two different thresholds for selection of the most likely target base station. Further, the potential improvement of the prediction efficiency via techniques originally proposed for minimizing the number of redundant handovers is analyzed. The efficiency of the proposed prediction technique is evaluated and compared with other prediction techniques based on channel characteristics in scenarios according to IEEE 802.16m. The proposed technique achieves a prediction hit rate of up to 93%.
Deployment of femtocells with open or hybrid access into mobile networks leads to problems with handling handover of mobile users. In this paper we discuss two important aspects
that can be considered and exploited in a design of innovative handover procedure for scenario with femtocells. The first aspect is related to varying quality and limited capacity of femtocell's
backbone. Further, an accuracy of determination of a time spent by users in a femtocell is analyzed in this paper with purpose to exploit it for elimination of redundant handovers. The
implementation aspects of proposed enhancements in handover decision algorithm are discussed as well.
Optimization of Power Control Algorithm for Femtocells Based on Frame Utilization
The paper is focused on power adaptation algorithm based on frame utilization of femtocells. To decrease its computational complexity and to minimize generated signaling overhead, adaptation interval should be prolonged. The problem of existing power adaptation algorithm is an increase of the number of mobility events with extension of adaptation interval length. Thus, the main objective is to propose a new adaptation algorithm able to cope with this problem. The possible disadvantage of new algorithm consists in longer interval of femtocell's overloading. We suggest mitigating this drawback by appropriate selection of target frame utilization, which is also contemplated in this paper. The performance of the algorithm is analyzed in terms of number of generated mobility events and femtocell's overloading time. The results show that the proposed adaptation algorithm outperforms the existing one for longer adaptation periods.
Different techniques for error correction such as automatic repeat request, forward error correction or combination of both methods also known as hybrid automatic repeat request are utilized in today's wireless networks. All these techniques require an implementation of control and management mechanisms to ensure its proper work. These mechanisms increase the management overhead and consume a part of network capacity. It leads to the reduction of data throughput dedicated to users. Besides, the control mechanism can negatively influence a packet delay. This paper proposes three alternatives of automatic repeat request mechanism to minimize the management overhead without a negative impact on the packet delay. Description of all proposed mechanisms, their evaluation and comparison with conventional techniques used in the IEEE 802.16 networks are presented in this paper.
Path Selection in WiMAX Networks with Mobile Relay Stations
Introduction of mobile relays into networks based on IEEE 802.16 standard brings new challenges. The paper proposes signaling mechanism for acquisition of channel state
information for mobile relays and provides detail analysis of the amount of signaling overhead caused by their introduction. In addition, the investigation whether the connection of the
mobile users through the mobile relays enhances the system performance is carried out.
QoS-Guaranteed Power Control Mechanism Based on the Frame Utilization for Femtocells
The paper focuses on a power control mechanism and proposes a novel approach for dynamic adaptation of femtocells' transmitting power. The basic idea is to adapt the transmitting power of femtocells according to current traffic load and signal quality between user equipments and the femtocell in order to fully utilize radio resources allocated to the femtocell. The advantage of the proposed scheme is in provisioning of high quality of service level to the femtocell users, while interference to users attached to macrobase station is minimized. The paper proposes the power adaptation algorithm and evaluates its performance in terms of mobility events, achieved throughput, and FAPs transmitting power. Performed simulations show that the proposed scheme can significantly reduce the number of mobility events caused by passerby users and thus to minimize signaling overhead generated in the network.
Adaptive Hysteresis Margin for Handover in Femtocell Networks
The purpose of this paper is to propose mechanism with minimum requirements on conventional network and user's equipment and with a simple implementation. Evaluations of proposal in term of efficiency of the redundant handovers reduction as well as an impact on
the user's throughput in 4G wireless networks are carried out. The results show significant reduction of the amount of handovers while reducing the impact on the throughput.
Creating the Neighbor Cell List of New-installed Femtocell
To enable large deployment of so called femtocells into conventional mobile wireless networks, several problems related to their implementation must be solved. The most of the problems are related to the fact that the customer can place femtocell anywhere in his premises. This paper examines an automatic creation of a Neighbor Cell List for recently deployed femtocells. Furthermore, the minimization of delay caused by the scanning of radio frequencies with purpose to find a suitable candidate for handover is proposed. The results of the simulation show that the proposed mechanism enables fast adaptation of new femtocell into an existing environment and reaches lower delay than conventional approaches.
Dynamic Power Control Mechanism for Femtocells Based on the Frame Utilization
The paper focuses on a power control mechanism and proposes a novel approach for dynamic adaptation of transmitting power of femtocell access points. The basic idea is to adapt the
transmitting power of femtocells according to current traffic load and signal quality between mobile stations and femtocell in order to fully utilize data frame.
Femtocells for next-G Wireless Systems: the FREEDOM approach
The paper deals with femtocell in context of next generation wireless systems. Actually, in the framework of the ITU, the definition of IMT.ADVANCED systems is on-going and all the candidates already investigate the usage of femtocells to improve system capacity. On the other hand, femtocell market is still at its early stage, facing the competition of low-cost, easy to use WiFi equipments. This paper presents the concept of femtocell and the related challenges and promising technical solutions to make them happen in the framework of next generation of broadband OFDM system.
Innovation of the Subject Telecommunication Systems and Networks
This paper deals with innovation of the subject Telecommunication Systems and Networks taught in summer semester at Czech Technical University in Prague. The innovation contains the creation of new practical exercises and preparation of new electronic multimedia lectures. All new aspects are focused on the enhancement of courses by the novel information on the wireless networks.
Reduction of Scanning Reporting Overhead in IEEE 802.16 Networks with Relays
A monitoring of signal quality must be performed in wireless networks since the users are continuously moving among base or relay stations. This is done via so called scanning procedure when a mobile station measures specific channel parameters. After each scanning, the mobile station reports the results to its serving station. This paper proposes a new method for delivering of scanning results to the serving base station if relay stations are deployed in network. The main goal of the proposal is to design a reporting technique that generates minimum management overhead during reporting. This is achieved by collecting of individual MSs' results into one message in access station. The results show reduction of scanning reporting management overhead up to 30%.
Acquisition of Channel State Information for Routing Purposes in Relay-based WiMAX Networks
The paper focuses on multi-hop routing mechanism
used in relay based WiMAX networks. The proposal how to
acquire channel state information (CSI) on relay and access
path for de-centrally and centrally controlled relays is presented. Furthermore, on the access path the proposal distinguishes whether the user's terminals are active or inactive in order to save system resources. Based on simulation, the protocol overhead of proposed mechanism for various system configuration and parameters such as nominal channel bandwidth, number of users in the system or reporting period is evaluated. Additionally, an optimum reporting period
for the system capacity maximization is determined.
Efficiency of Handover Prediction Based on Handover History
The prediction of the handover in mobile wireless networks is an easy way of optimal disposing with radio resources and efficient increase of quality of services. The prediction can be based on the several approaches. This paper is focused on the monitoring of the history of serving and target base stations while the handover is executed. The information about previous handovers of all users are stored in base stations. Then the prediction is performed based on the frequency of previous handovers between pairs of base stations. The paper investigates an efficiency of target base station prediction for several scenarios. Further, an impact of a number of neighbor stations on the ratio of successfully predicted target base stations is analyzed.
Proceedings of the 2009 Networking and Electronic Commerce Research Conference (NAEC 2009). Dallas, TX: American Telecommunications Systems Management Association Inc., 2009. pp. 85-91. ISBN 978-0-9820958-2-9.
The primary objective of this paper is to deepen the methodology for objective quantification of the quality of transmission through the impairment factors closely associated with the utilization of wideband speech codecs in telecommunication networks. This methodology is based on subjective auditory listening-only tests, but the resulting impairment factors may be used for predicting speech quality in an instrumental way, e.g., for network planning purposes. The derived impairment factors fit into the common framework which is defined in recommendations by the E-model for narrowband telephone networks, and which is hereby extended towards wideband speech transmission.
Impact of Handover on VoIP Speech Quality in WiMAX Networks
VoIP is one of the most emerging technologies in the area of speech communications. VoIP is widely deployed in fixed line access networks. However, user's requirements on the mobility within communication and on the quality of the speech communications are increasing. Therefore, VoIP technology is more and more integrated into broadband wireless networks with QoS support such as WiMAX. The latest version of WiMAX standard, based on IEEE 802.16e,
has implemented a full mobility support. The mobility is allowed due to a handover procedure. IEEE 802.16e specifies only one mandatory handover procedure: hard handover. This handover type is easy to implement, but it increases end-to-end packet delay that is critical for delay sensitive services such as VoIP. This paper evaluates an impact of the handover on the
speech quality in VoIP networks.
Initialization of Handover Procedure in WiMAX Networks
This paper introduces a new approach in the triggering of handover procedure in WiMAX networks. Mobile WiMAX, based on the IEEE 802.16e standard, supports a several types of handovers and allows full mobility of users. The originating IEEE 802.16j standard introduces new network entities called relay stations. A relay station enables either throughput enhancement in the selected area or enlargement of a coverage area of base station.
Článek se zabývá mechanismem pro opravu chyb ARQ (Automatioc Repeat reQuest) v bezdrátových sítích založených na technologii WiMAX. Dále jsou v něm prezenovány výsledky měření vlivu ARQ na přenosovou rychlost mezi základnovou stanicí (BS) a účastnickou stanicí (SS) v závislosti na útlumu přenosového kanálu pro různé modulace.
Contemporary Next Generation Networks (NGN) are mainly built on Internet Protocol (IP). Major challenge for emerging types of wired and wireless IP-based networks is to provide adequate Quality of Service (QoS) for different services. Quality evaluation requires a detailed knowledge of the performance requirements for particular services and applications. The starting point for deriving these performance requirements must be the user.
The FP7 ROCKET Project: Wireless Access Technology for Homogeneous High Rate Coverage
Simoens, S., Labbe, P., Vidal, J., Hoshyar, R., Augustin, A., Thilakawardana, S., doc. Ing. Zdeněk Bečvář, Ph.D., Wolz, B., Sambale, K., Panagiotopoulos, I., De Marinis, E., Soro, D., Rudant, L.
ICT-MobileSummit 2008 Conference Proceedings. Dublin: IIMC International Information Management Corporation Ltd, 2008. pp. 78-87. ISBN 978-1-905824-08-3.
The FP7 ROCKET project aims at designing Broadband Wireless Access (BWA) technology that enables a larger number of users to be served at a high data rate in both urban and rural deployment scenarios. The technical approach relies on the combination of 1)advanced coordination and cooperation of Base Stations (BS) and Relay Stations (RS) in order to increase the area spectral efficiency 2)operation on larger bandwidth, including an opportunistic/flexible spectrum usage that adapts to the different capabilities of the network devices (BS,RS,MS) 3)optimized signaling that minimizes the MAC overhead for basic functionalities such as scheduling and handover and the backhaul load required to support the advanced spectrum usage, mobility (i.e. handover) and cooperation mechanisms defined in the project. Finally, the antenna integration constraints and the implementability of selected key algorithms will be studied thanks to a state-of-the-art hardware and software prototyping platform.
Comparison of Common PLC Methods Used in VoIP Networks
VoIP is very emerging technology in last years and telecommunication operators seek to profit from it. The main advantage of this technology is usage of existing infrastructure in the form of wide coverage of Internet connection. Unfortunately, this advantage brings some weak points that are expectable because of the quality of Internet connection. The quality of a signal on a receiving side is affected by many disturbances that have to be suppressed. This paper deals with some methods which are used for suppressing of consequences of these effects. There were compared several methods implemented only on the receiving side of telecommunication chain. The efficiency of these methods was assessed by both subjective and objective tests.
Evaluation of 3SQM Algorithm Performance on Fixed-Point DSP
The WiMAX networks are generally based on the 802.16 standards. There exist several versions of this standard which differs mainly in the mobility support. It can be realized by several different handover procedures. There can occur occasional problems with accurate determination of proper signal level values within network operating. These values are used for performing handover. It is possible to suppress these problems by implementing Handover Delay Timer. This paper deals with the impact of this timer duration on the number of unnecessary handover initializations, which are caused by inaccurate signal level assessment.
Implementation of New Practice Exercises into the Digital Signal Processing in Telecommunications