Publikace

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 offloading macrocells.

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.

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.

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.

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%.

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.

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.

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.

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.

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.

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

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.

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%.