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.

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.

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.

The caching paradigm has been introduced to alleviate backhaul traffic load and to reduce latencies due to massive never ending increase in data traffic. To fully exploit the benefits offered by caching, unmanned aerial vehicles (UAVs) and device-to-device (D2D) communication can be further utilized. In contrast to prior works, that strictly limits the content delivery routes up to two hops, we explore a multi-hop communications scenario, where the UAVs, the UEs, or both can relay the content to individual users. In this context, we formulate the problem for joint route selection and power allocation to minimize the overall system content delivery duration. First, motivated by the limitations of existing works, we consider the case where the nodes may transmit content simultaneously rather than sequentially and propose simple yet effective approach to allocate the transmission power. Second, we design a low-complexity greedy algorithm jointly handling route selection and power allocation. The simulation results demonstrate that the proposed greedy algorithm outperforms the benchmark algorithm by up to 56.98% in terms of content delivery duration while it achieves close-to-optimal performance.

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.

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.

This paper addresses the monitoring of Varroa destructor infestation in Western honey bee colonies. We propose a simple approach using automatic image-based analysis of the fallout on beehive bottom boards. In contrast to the existing high-tech methods, our solution does not require extensive and expensive hardware components, just a standard smart-phone. The described method has the potential to replace the time-consuming, inaccurate, and most common practice where the infestation level is evaluated manually. The underlining machine learning method combines a thresholding algorithm with a shallow CNN—VarroaNet. It provides a reliable estimate of the infestation level with a mean infestation level accuracy of 96.0% and 93.8% in the autumn and winter, respectively. Furthermore, we introduce the developed end-to-end system and its deployment into the online beekeeper’s diary—ProBee—that allows users to identify and track infestation levels on bee colonies.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.