Ing. Jan Plachý, Ph.D.

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.

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.

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.

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 %.

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.

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.

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.

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.

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.

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%.

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.

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%.

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.