In this paper, we focus on a cache-enabled networks, where a content requested by user equipments (UEs) can be delivered by means of multi-hop relaying via unmanned aerial vehicles (UAVs) and/or via other UEs exploiting device-to-device (D2D) communication. For such scenario, we optimize transmission power allocation, bandwidth allocation, and route selection to minimize the content delivery duration. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem and, thus, it is hard to solve. Hence, we first solve the power and bandwidth allocation by a cooperative game among all transmitting nodes involved in the content delivery process and we prove that this game is a potential game. Then, we introduce a fast-converging iterative algorithm employing an approximate better-response mechanism for the resource allocation (i.e., power and bandwidth). After that, we design a low-complexity greedy algorithm jointly handling route selection and power and bandwidth allocation. The algorithm selects transmission routes to deliver the requested contents to the UEs and, after each selection, the resource allocation at the transmitting nodes involved in the content delivery process (i.e., GBS, UAVs, and relaying UEs) is updated. The simulation results demonstrate that the proposed scheme reduces the average content delivery duration by up to 23.2% compared to the best-performing benchmark algorithm.

Efficient radio resource management for Device-to-Device (D2D) communication is challenging due to the need to satisfy diverse Quality of Service (QoS) requirements for various applications. While machine learning techniques have gained attention for addressing these issues, joint prediction of multiple radio resource parameters, such as transmission power, or bandwidth allocation, often leads to suboptimal performance. Thus, in this paper, we propose a coordinated multi-task learning approach based on deep neural networks for joint prediction of channel quality, power, and bandwidth allocation for D2D communication. Then, we design task-specific loss functions and their mutual coordination to maximize sum capacity, ensure QoS, and satisfy task-specific constraints, such as limits on transmission power and bandwidth. Simulation results demonstrate that the coordinated multi-task learning improves the sum capacity and the ratio of devices satisfied with capacity by up to 54% and 30%, respectively, compared to state-of-the-art techniques.

Spectral efficiency in mobile networks can be increased by allowing devices communicating directly with each other in a form of device-to-device (D2D) communication to reuse channels assigned to cellular devices, i.e., devices communicating via base station. However, the resources reuse leads to additional interference between the cellular and D2D devices. This interference can be suppressed by a smart selection of channels to be reused and allocation of transmission power to all devices at the reused channels. Since this problem is NP-hard, we propose a solution based on deep deterministic policy gradient (DDPG) for cellular channel reuse decisions combined with deep neural network (DNN) for transmission power allocation to all devices. Both machine learning models (DDPG and DNN) are naturally sub-optimal. Thus, we further extend the work towards coordinated learning of both DDPG and DNN so that a potential performance degradation due to sub-optimal outputs of DNN and DDPG is suppressed via a mutual interaction between DDPG and DNN. Simulation results show that proposed solution boosts sum capacity by up to 63 % compared to the best-performing state-of-the-art work.

We focus on computation offloading from moving devices, such as mobile robots or autonomous vehicles to MultiAccess Edge Computing (MEC) servers via mobile network. To this end, we develop and implement a prototype of small autonomous vehicle with capability to offload processing of sensor data to MEC server via mobile network. Then, we investigate an impact of communication channel on delay and energy consumed by the autonomous vehicle for two practical applications, namely road sign recognition and path planning, in the real-world environment with a real physical equipment. Via experiments, we demonstrate benefits of the computation offloading on both energy and delay. The experiments highlight the potential of MEC for the autonomous systems allowing to reduce cost and increase scalability of such autonomous systems. Furthermore, based on the real-world experiments, we derive detailed models of energy consumption and delay for both practical applications.

In this paper, we propose a framework for semantic information sharing between vehicles to reduce energy consumed by computation related to processing of sensor data. The energy consumption is reduced via avoiding redundant semantics extraction by multiple vehicles. We develop an algorithm that allows sharing semantic information derived by one vehicle with neighboring vehicles, thereby reducing the need for individual semantics extraction. Of course, semantic sharing introduces communication energy overhead. Thus, in our work, we consider not only computation but also communication energy. We propose graph representation of the problem to allow mapping of a part of the energy consumption minimization to the maximum independent set problem, which we further combine with a greedy and recursive approach. Simulations demonstrate that the proposed algorithm can save up to 61% of energy compared to state-of-the-art approaches.

In this paper, we propose novel architecture supporting artificial intelligence-based multimodal semantic communication and distributed computing. The proposed architecture is built on existing concepts in adopted mobile networks, including cloud radio access network (C-RAN) and open-RAN (O-RAN). On top of current architectures, we introduce new key features including block for artificial intelligence (AI) training models for semantic encoding and decoding, semantic modules, and hierarchical edge cloud for distributed and parallel processing. Then, we formulate a delay minimization problem for processing of semantically encoded tasks by the hierarchical edge cloud. First, we derive optimal closed-form solutions for splitting the tasks between individual tiers of the hierarchical edge cloud while assuming actual communication and computing queues. Second, we propose a low-complexity algorithm selecting place, where the individual tasks are processed while adopting the optimal splitting of the tasks. Via simulations, we demonstrate that the proposed solution decreases average processing time and energy consumption due to computing by up to 50 % and 23 % when compared to the best performing state-of-the-art scheme.

Seamless connectivity and efficient mobility management in mobile networks can be facilitated via estimation of the speed and mobility state of user equipment (UE). In this paper, we propose a deep reinforcement learning (DRL) framework for prediction of the UE speed based on the number of performed handovers and density of all base stations (BSs), including both macro base stations (MBSs) and small-cell base stations (SBSs). We leverage the capability of DRL to learn and adapt to complex mobility patterns, enhancing the accuracy of the speed prediction in highly dynamic environments. To demonstrate benefits of the DRL-based UE speed prediction, we further exploit the predicted speed to determine the UEs’ mobility states. The mobility state serves as an input for handover optimization via fuzzy logic. By incorporating the speed prediction, the system proactively anticipates changes in the UEs’ mobility and enables more precise and timely optimization of handover parameters. The simulation with realistic UE mobility traces shows that the proposed DRL improves the accuracy of the speed prediction by up to 35% and reduces the prediction root mean square error by up to 59% compared to the state-of-the-art algorithms. Moreover, using the proposed DRL-based speed prediction for fuzzy logic-based handover optimization improves the UEs’ sum capacity by up to 43% and reduces the number of handovers by up to 39% compared to state-of-the-art works.

In this paper, we focus on a cache-enabled multi-hop network, where the unmanned aerial vehicles (UAVs), the user equipment (UEs), or both can serve as relays to deliver contents to individual users from a ground base station (GBS). We formulate a power optimization problem with the objective to minimize the sum content delivery delay. We show the optimization problem is non-convex, thus, we propose a novel heuristic algorithm to allocate the power to individual contents at individual hops in multi-hop scenario. The proposed heuristic algorithm iteratively re-allocates the transmission power among the contents at the same transmitting node. To reduce the number of iteration, the proposed algorithm enables parallel power re-allocation for multiple content pairs and dynamic adaptation of power re-allocation steps, thereby enabling faster convergence of the algorithm. We demonstrate that our proposal reduces the average content delivery duration by up to 31.8 % compared to state-of-the-art works. At the same time, proposal is suitable for real systems due to a very fast convergence.

In this paper, we focus on the scenario with offloading of computationally intensive tasks with delay constraints from users equipment (UEs) to multi-access edge computing (MEC) servers. To avoid user's dissatisfaction with offered quality of service, the computing resources should be able to handle even peak hours. As a result, a dense deployment of MEC servers should be considered in order to bring sufficient computing resources close to the UEs, thus enabling a low delay services. However, at the same time, the dense deployment of powerful MEC servers results, among others, in a high energy consumption. In this paper, we address the high energy consumption problem via a smart sleeping of the MEC servers while preserving quality of service for the UEs. To this end, we determine a set of MEC servers that should stay active and provide computation resources for the offloaded tasks while still meeting UEs requirements on delay. We formulate the problem of selecting the MEC server that can be set into sleep mode to save energy as a minimum set cover problem. Then, we propose a solution to minimize the energy consumption based on branch-and-bound algorithm to activate the MEC servers for computation ensuring the UEs requirement on delay. The effectiveness of the proposed solution is demonstrated through simulations showing that the proposal allows to save up to 34.9% of energy compared to state-of-the-art works while even slightly improving the ratio of offloaded tasks processed within required delay.

In this paper, we introduce a novel framework jointly managing handovers of user equipments (UEs) and Unmanned Aerial Vehicles (UAVs) serving the UEs. The goal is to maximize the sum capacity of the UEs while considering a cost related to the handovers. To this end, we introduce a novel approach based on deep deterministic policy gradient (DDPG) adjusting the Cell Individual Offset (CIO) for handovers of the UEs among the UAVs and ground base stations (GBSs) as well as handovers of the UAVs among the GBSs. The UAVs playing the role of relays often face challenges related to the implementation cost and energy limitations. To address these challenges, the UAVs should operate in a transparent relaying mode. In such mode, unfortunately, the channels between the UEs and the UAVs are unknown as the transparent relays lack any communication control-related functionalities. Therefore, we adopt a deep neural network (DNN) to predict the channel qualities among the UEs and the UAVs for the handover purposes. We demonstrate that the proposal significantly increases the sum capacity of the UEs by dozens of percent and even reduces the number of handovers compared to state-of-the-art works. At the same time, the proposed DDPG-based CIO setting reduces a gap in the sum capacity between the predicted and the optimal (but practically not feasible) case with perfectly known channels among UEs and UAVs. Hence, the proposal is suitable for practical scenarios with not perfectly accurate channel quality information.

We focus on handover management in networks integrating traditional terrestrial ground base stations (GBSs) and non-terrestrial unmanned aerial vehicles (UAVs) serving users. In such scenario, we propose a joint management of handover of users equipment (UEs) between UAVs and GBSs as well as handover of UAVs between GBSs. Our goal is to maximize the sum capacity for UEs while avoiding redundant handovers. As an impact of handover on the future network performance is not explicit, we adopt deep deterministic policy gradient (DDPG). Furthermore, since the UAVs are usually energy constrained, we consider an energy efficient transparent relaying mode for the UAVs. However, in the transparent relaying mode, the access channel quality between the UAV relay and the UE is unknown even if such information is essential for handover. Thus, we further employ deep neural network (DNN) to predict the access channel quality. An incorporation of DDPG for handover management together with DNN for channel quality prediction can impair the sum capacity of the UEs due to an accumulation of inherent small prediction errors of DNN and DDPG. Hence, we also introduce a coordination between DNN and DDPG to suppress the accumulation of the prediction errors. Simulations demonstrate that the proposed handover management and the coordination of DDPG and DNN increase the sum capacity by up to 63% while notably reducing the number of handovers and handover failure ratio compared to the state-of-the-art works.

Low-latency Vehicular Edge Computing (VEC) applications require an efficient VEC resource allocation considering all components contributing to application latency, i.e., computation, communication, and storage. While the optimization of communication and computation resources is broadly addressed in literature, storage, a significant source of latency in the computing stack, is often ignored in existing works. Thus, in this paper, we optimize the communication and computation resources together with the storage resources to minimize the latency of VEC applications. The problem of jointly minimizing communication, computation, and storage latency under practical constraints is NP-hard. Hence, we employ dual decomposition and Lagrangian relaxation to achieve a computationally viable solution for the joint communication, computing, and storage resource allocation to VEC applications. To this end, we define a dual problem of the assignment of VEC applications to base stations. This problem corresponds to the perfect matching problem in a weighted bipartite graph and optimally solvable by algorithms with polynomial computation complexity. Then, as the solution to the dual problem may violate some constraints of the main resource allocation problem, we find a feasible solution to the main resource allocation problem using Lagrangian relaxation. We show that the joint optimization of all three aspects, i.e., communication, computation, and storage, reduces the overall offloading latency up to 60% compared to state-of-the-art works.

Hyperconverged Infrastructures(HCIs) combine processing and storage elements to meet the requirements of data-intensive applications in performance, scalability, and quality of service. As an emerging paradigm, HCI should couple with a variety of traditional performance improvement approaches such as I/O caching in virtualized platforms. Contemporary I/O caching schemes are optimized for traditional single-node storage architectures and suffer from two major shortcomings for multi-node architectures: a) imbalanced cache space requirement and b) imbalanced I/O traffic and load. This makes existing schemes inefficient in distributing cache resources over an array of separate physical nodes. In this paper, we propose anEfficient andLoad BalancedI/OCacheArchitecture(ELICA), managing thesolid-state drive(SSD) cache resources across HCI nodes to enhance I/O performance. ELICA dynamically reconfigures and distributes the SSD cache resources throughout the array of HCI nodes and also balances the network traffic and I/O cache load by dynamic reallocation of cache resources. To maximize the performance, we further present an optimization problem defined byInteger Linear Programmingto efficiently distribute cache resources and balance the network traffic and I/O cache relocations. Our experimental results on a real platform show that ELICA improves quality of service in terms of average and worst-case latency in HCIs by 3.1× and 23%, respectively, compared to the state-of-the-art.

Mutual reuse of communication channels among device-to-device (D2D) pairs enhances the spectral efficiency of the mobile networks. However, the interference among D2D pairs mutually reusing the same channels imposes a significant challenge. In combination with allocation of the transmission power of each pair for the reused channels, the problem of joint D2D channel reuse and transmission power allocation becomes NP-hard. Thus, we employ deep deterministic policy gradient (DDPG) to decide how the D2D channels should be reused by the D2D pairs. Then, for the reused channels, we allocate the transmission power of the D2D pairs sharing the channels using deep neural network (DNN). However, combining the DDPG-based channel reuse with the DNN-based transmission power allocation leads to an accumulation of errors introduced by DDPG and DNN. The accumulated errors degrade the overall communication capacity. Thus, we also introduce a coordination between DNN and DDPG to suppress the effect of the error accumulation. Simulation results demonstrate that the proposed DDPG-based channel reuse even without coordination increases the sum capacity by 15% compared to state-of-the-art works. On top of this gain, the coordination of both DDPG and DDN adds another 12% in the sum capacity.

Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities - Prague and Munich. We focus on the robot's ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system's holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial.

This paper addresses the challenge of optimizing communication, computation, and storage I/O caching in Vehicular Edge Computing (VEC) platforms for autonomous vehicles. The exponential data generated by the autonomous vehicles demands low-latency connectivity with nearby edge servers. However, the existing VEC platforms struggle to meet the performance requirements, especially in real-time applications like collision avoidance. This work proposes a novel algorithm for joint allocation of computing resources, storage I/O cache, and communication resources, considering the diverse priorities and demands of key vehicular services. Our approach integrates application-specific optimizations, prioritization, and joint latency reduction considering communication, computation, as well as storage. Accounting for distinct priorities and data access characteristics of various vehicular services, our proposed feasible solution, employing dual decomposition and Lagrangian relaxation, significantly reduces service latency by up to 64% compared to the current state-of-the-art resource allocation in vehicular edge computing.

User applications based on the deep neural networks (DNNs), such as object or anomaly detection, image recognition, or language processing, running on computation- and energy-constrained user equipment (UE) can be partially or fully processed in the edge computing servers to reduce a processing time and save an energy in the UE. To further reduce the processing time and the UE’s energy consumption, DNN with multiple exit points can be incorporated. In this article, we address the problem of the decision on whether the computation should be offloaded from the UE to the edge computing server or processed locally by the UE and we solve this problem jointly and “on-the-fly” together with DNN exit selection. Since the formulated problem is very complex, we exploit the deep deterministic policy gradient for the exit selection and the offloading decisions (labeled DDPG-EOD) for the DNN-based applications. To this end, we first convert the problem into the Markov decision process, and then, we employ an end-to-end learning via DDPG with the actor-critic architecture. Second, we use a knowledge distillation-based technique to efficiently select the DNN’s exit to minimize the delay and energy consumption. Simulation results show that the proposal is highly scalable, converges very quickly, and surpasses the best performing state-of-the-art approach by up to 120% and 100% in terms of the overall DNN processing delay and the energy consumption, respectively.

This paper investigates the performance of cellular networks assisted by unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs). We focus on a scenario with multi-hop relaying via FlyBSs to deliver data from a ground base station (GBS) to users in a challenging case with the channels reused at all hops to exploit radio resources efficiently. Our objective is to maximize the sum capacity of the users via an optimization of FlyBSs’ position in 3D, association of users to either GBS or to one of the FlyBSs, allocation of channels for communication at individual hops, and allocation of transmission power for all channels. Moreover, practical constraints on the FlyBSs’ movement, transmission and propulsion power, and backhaul capacity are taken into account. Due to a non-convexity and discreetness of the objective and some constraints, there is no optimal solution to the formulated problem. Thus, we propose an analytical solution based on an alternating optimization of an energy-efficient placement of the FlyBSs, channel allocation, user association, and transmission power. Each subproblem in the alternating optimization is substituted either by a linear programming (LP) problem through a change of variables, or by a convex problem via a conversion of the objective and constraints. The results show an increase in sum capacity by 35%–60% compared to related works while the FlyBSs’ propulsion power consumption is not increased.

The latency associated with accessing data stored on edge computing servers for vehicles encompasses both the communication between a vehicle and a server as well as a latency of a data storage system. To enable low-latency vehicular services, an efficient resource management should consider the communication as well as the storage I/O cache resource allocation along with a data access pattern and a priority of individual vehicular services. Therefore, we focus on a joint optimization of communication and storage I/O cache resource allocation for access to data of vehicular services hosted by the edge computing servers. The proposed framework determines the data placement for the services and allocates communication and storage I/O cache resources to each service. The objective is to minimize the overall latency experienced by vehicular services for access to data. The edge computing platforms share storage and communication resources among various vehicular services, each having distinct priorities and data access rates or patterns. Hence, to reflect different priorities of services in resource allocation, our objective metric takes into account the service priority, data access frequency, and latency. We propose a feasible solution using dual relaxation considering both communication and storage latencies. The proposed solution reduces the average latency of vehicular services by up to 1.8x compared to the state-of-the-art resource allocation method for vehicular edge computing. Even more notable improvement is observed for high priority vehicular services, where the proposal leads to 2.5x lower latency compared to the state-of-the-art storage I/O cache architecture for virtualized cloud services.

We focus on prediction of channel quality between any two devices using Deep Neural Network (DNN) from information already known to mobile networks. The DNN-based prediction reduces a cost of a common pilot-based channel quality measurement in scenarios with many ad-hoc communicating devices. However, collecting a sufficient number of high-quality and well-distributed training samples in real-world is not feasible. Hence, in this paper, we develop and validate a concept of DNN-based channel quality prediction between any two devices based on a low-complexity and easy-to-create digital twin. The digital twin serves for a generation of a large synthetic training dataset for channel quality prediction. As the low-complexity digital twin cannot capture all real-world aspects of the channels, we enhance the digital twin with real-world measured and artificially augmented inputs via transfer learning. The proposed concept is implemented and validated in software defined mobile network. We demonstrate that the proposed concept predicts the channel quality with a very high accuracy (mean average error of only 0.66 dB) in a real-world complex indoor scenario. Such error is sufficient for practical applications of the developed channel quality prediction concept and the error is few times lower than the error achievable by state-of-the-art solutions.

In mobile networks, unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) can effectively improve performance. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we study the problem of sum downlink capacity maximization in FlyBS-assisted networks with mobile users and with a consideration of wireless backhaul with channel reuse while a minimum required capacity to every user is guaranteed. The problem is formulated under constraints on the FlyBS's flying speed, propulsion power consumption, and transmission power for both of flying and ground base stations. None of the existing solutions maximizing the sum capacity can be applied due to the combination of these practical constraints. This paper pioneers in an inclusion of all these constraints together with backhaul to derive the optimal 3D positions of the FlyBS and to optimize the transmission power allocation for the channels at both backhaul and access links as the users move over time. The proposed solution is geometrical based, and it shows via simulations a significant increase in the sum capacity (up by 19%-47%) compared with baseline schemes where one or more of the aspects of backhaul communication, transmission power allocation, and FlyBS's positioning are not taken into account.

With the advent of connected and autonomous vehicles (CAVs), we observe a growing need for new resource allocation solutions in mobile networks. Currently, most of the resource allocation solutions for CAVs communication do not consider the driving routes of the cars. In this paper, we introduce joint vehicular route selection and radio and computing resource allocation for CAVs. The proposed approach is based on the graph search-based lexicographic A* algorithm that minimizes the ratio of failed tasks along the entire vehicular route considering the availability of both radio and computing resources. To manage the allocation of resources among multiple CAVs for each vehicular route, we develop a blockchain-based framework allowing resource reservation by means of nonfungible tokens (NFTs). Each NFT represents an exclusive right to the required amount of radio and computing resources for the given road segment and defined time interval. The effectiveness of the proposed approach is demonstrated by simulations showing that the proposed vehicular route selection algorithm reduces the ratio of tasks not completed before the deadline by up to 69% compared to the existing state-of-the-art algorithms.

To ensure a seamless mobility of users in the scenario with flying base stations (FlyBSs) and static ground base stations (GBSs), an efficient handover mechanism is required. In this paper, we introduce new framework simultaneously managing cell individual offset (CIO) for handover of both FlyBSs and mobile users. Our objective is to maximize capacity of the mobile users while considering also a cost of handover to reflect potential excessive signaling and energy consumption due to redundant handovers. This problem is of a very high complexity for conventional optimization methods and optimal solution would require knowledge of information commonly not available to the mobile network. Hence, we adjust the CIO of FlyBSs and GBSs via reinforcement learning. First, we adopt Q- learning to solve the problem. Due to practical limitations implied by a large Q-table, we also propose Q- learning with approximated Q-table. Still, for larger networks, even the approximated Q-table can require a large storage and computation time. Therefore, we apply also actor-critic-based deep reinforcement learning. Simulation results demonstrate that all three proposed algorithms converge promptly and increase the communication capacity by dozens of percent while the handover failure ratio and the handover ping-pong ratio are reduced multiple times compared to state-of-the-art.

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 an analytical 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 capacity at 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.

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 proposed solution is of a low complexity and increases the sum capacity by 15% -46% comparing to state-of-the-art works.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Device-to-device communication (D2D) is expected to accommodate high data rates and to increase the spectral efficiency of mobile networks. We focus on the dedicated mode where D2D pairs exploit channels that are different from the channels allocated to conventional cellular users. Such mode is suitable for scenarios of 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 the reuse of multiple channels by multiple D2D pairs in order to increase the throughput of 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 throughput gain of 28-64% comparing to the best performing existing algorithm.

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 real-time applications. According to the simulations, the proposed method brings up to 31% of FlyBS's transmission power saving compared to existing solutions.

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.

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.

Device-to-device (D2D) communication, which enables 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.

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.

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.

Deployment of unmanned aerial vehicles (UAVs) in future mobile networks has recently been considered as a reliable technique to enhance capacity of the network and to facilitate an efficient communication in emergency cases. The improvement provided by such network depends on the deployment of the UAVs with respect to users and also on the power consumption of the UAV. In this paper, we study the power consumption in the wireless networks equipped with the UAVs. We propose the novel solution in which the UAV can either change its transmitting power or relocate itself to a new position as the users move in order to guarantee quality of service to the users. We analytically find the transmitting power and an ideal position of the UAV to minimize the total consumed power by the UAV consisting of the power for communication and the power for a displacement of the UAV. According to the simulations, the proposed scheme brings up to 30% of total UAV power saving in the scenario with users moving in crowd.

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.

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 multi-objective 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.

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 above-mentioned 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.

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.

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.

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.

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 Virtualisation 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.

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.

Abstract 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. Copyright © 2013 John Wiley & Sons, Ltd.

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.

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.

To ensure faultless handover procedure of mobile users, each cell in the network must establish a Neighbor Cell List (NCL). The NCL contains adjacent cells to which handover can be performed. A problem related to the handover from a Macro/Micro cell (MeNB) appears with the rising density of cells with the limited coverage, so-called Small Cells (SCeNBs). By using method of the NCL creation described in standards or literature, the number of neighboring cells suitable for handover from the MeNB can be extensive. Therefore, we propose an algorithm for automatic creation of the NCL 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. As the results show, the proposed algorithm significantly reduces the number of cells in the NCL while the probability of missing handover target cell in the NCL is kept negligible.

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.

ABSTRACT 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 balancingviaRSs 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. Copyright © 2011 John Wiley & Sons, Ltd.

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.

The main requirement in the Voice over IP technology is a good quality of received voice signal during communication between subscribers. The signal quality can be influenced by many factors such as packet loss, jitter, packet delay, noise etc. and it can be measured by number of methods. The main purpose of this paper is the investigation of an impact of different noise types and different noise levels on the quality assessment in VoIP. The artificial generated noises and real noises obtained from real telecommunications networks were used for testing. The next goal is a comparison of the results obtained by subjective listening tests and objective measuring methods. PESQ and 3SQM were used for objective testing in this paper.