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An LLC dual active bridge (DAB) bidirectional converter with reduced switch count as a dc–dc transformer (DCX) is proposed in this article. It can achieve voltage regulation, full load range zero voltage switching (ZVS) for eight main switches and wide load range ZVS for four auxiliary switches, with consideration of the switch junction capacitors under different power direction. A part portion of total power is transferred through an auxiliary DAB circuit to achieve voltage regulation and bidirectional power conversion. The main power is transferred through LLC circuit with an easy PWM scheme achieving high conversion efficiency. Different from the existing LLC-DAB DCX structure, the two secondary side transformer outputs are in parallel, and connected to the same full-bridge circuit. In this way, the auxiliary DAB circuit can not only adjust the output voltage, but also can inject current into the secondary side to help achieving ZVS of switches without synchronous rectifier circuit. The converter operation principle is analyzed under different power directions, and ZVS is analyzed with the consideration of all the switch junction capacitors. Based on the analysis, the design consideration is introduced. Finally, a 2-kW prototype with 750 V input and 400 V output is built up to verify the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

In this paper, uplink transmission of a cell-free massive multiple-input multiple-output (CF-mMIMO) system coexisting with device-to-device (D2D) communication links is investigated, under the assumption that access points (APs) are equipped with low resolution analog-to-digital converters (ADCs). Lower bounds of achievable rates for both D2D users (DUEs) and CF-mMIMO users (CFUEs) are derived in closed-form, with perfect and imperfect channel state information. Next, in order to reduce pilot contamination, greedy and graph coloring-based pilot allocation algorithms are proposed and analyzed for the considered scenario. Furthermore, to control interference and improve the performance, two power control strategies are designed and their complexity and convergence are also discussed. The first power control strategy aims at maximizing CFUEs’ sum spectral efficiency (SE) subject to quality of service constraints on DUEs, while the second one maximizes the weighted product of CFUEs’ and DUEs’ signal-to-interference-plus-noise-ratios (SINRs). Numerical results show that the proposed pilot and power allocations bring a considerable improvement to the network SE. Also, it is revealed that the activation of D2D links has a positive effect on the system throughput, i.e. the network offloading ensured by the D2D links overcomes the increased interference brought by D2D communications. [ABSTRACT FROM AUTHOR]

A novel device-to-device (D2D) aided cooperative non-orthogonal multiple access (NOMA) scheme (termed as D2D-SG-NOMA) is proposed, where two similar gain (SG) near users (NUs) with the capability of D2D communication and one far user (FU) are served within two time slots. The NOMA pair is formed with a NU and the FU. The paired NU is employed as a decode-and-forward relay to assist FU. Contrarily, the unpaired NU can receive signals simultaneously from the base station (BS) and the other NU during the second time slot. Two different scenarios (i.e., S1 and S2) are investigated insightfully. In S1, the direct link between the BS and FU does not exist, whereas the direct link between the BS and FU exists in S2. The delay-tolerant capacity (DTC), outage probability, diversity order, and delay-limited capacity are investigated along with analytical formulation. The D2D-SG-NOMA achieves an increase of around 66% and 85% in DTC at 0 dB signal-to-noise (SNR) under S1 and S2, respectively than the existing NOMA scheme with successive relaying (termed as SR-NOMA). Contrarily, a reduced DTC improvement (i.e., around 3% and 10% in S1 and S2, respectively) is obtained at 40 dB SNR due to increased inter-symbol interference. [ABSTRACT FROM AUTHOR]

Energy harvesting (EH) endows device-to-device (D2D) communication and cellular equipment with the ability of continuous communication to provide internet-of-things (IoT) services in natural areas. While the available energy, which relies on EH, becomes an extra nonnegligible factor in resource allocation. Besides, we integrate uplink non-orthogonal multiple access (NOMA) with D2D communication to provide multiple access for D2D transmitters for more efficient IoT service and more efficient utilization of limited spectrum. In this scenario, ingenious resource allocation approach is a key focus for utilizing the advantages in energy and spectral efficiency. Aiming to investigate the inherent resource allocation issue, we set our goal as maximizing the energy efficiency for both NOMA-based D2D groups and cellular users (CUs), where the power and spectrum allocation are both considered. Then we propose a two-stage game approach, which is theoretically proved to be capable of obtaining the equilibrium and a stable result, to solve the unilateral energy efficiency maximization problems. Besides, an energy-aware screening method is proposed to reduce the computations based on the available energy of user equipment. Finally, the effectiveness of our proposed method is verified through elaborated simulation results. [ABSTRACT FROM AUTHOR]

Ubiquitous sensor-rich smartphones have promoted MCS, an emerging people-centric sensing paradigm for urban IoT. However, using cellular networks to transmit the big data collected by MCS would incur expensive financial costs for both participating phone users and the MCS organizer. A promising solution to this is to integrate the low-cost D2D communication into the MCS design. However, using D2D in MCS will require cooperative interactions among self-interested and strategic participating phone users, which significantly complicates the “human-in-the-loop” MCS design in both the digital and human dimensions, and brings a branch of new challenges: data communication and networking become more complex; D2D connections among opportunistically encountered phone users must be secure, fast, and user-transparent; and participating phone users need to be properly incentivized. In dealing with these challenges, this article covers recent developments of D2D-enabled MCS from both the theoretical and practical perspectives. Future research questions in this rapidly growing field are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, we address the optimization of the energy efficiency of underlay multicast device-to-device (D2MD) communications on cellular networks. In particular, we maximize the energy efficiency of both the global network and the individual users considering various fairness factors such as maximum power and minimum rate constraints. For this, we employ a canonical mixed-integer non-linear formulation of the joint power control and resource allocation problem. To cope with its NP-hard nature, we propose a two-stage semi-distributed solution. In the first stage, we find a stable, yet sub-optimal, channel allocation for D2MD groups using a cooperative coalitional game framework that allows co-channel transmission over a set of shared resource blocks and/or transmission over several different channels per D2MD group. In the second stage, a central entity determines the optimal transmission power for each user in the system via fractional programming. We performed extensive simulations to analyze the resulting energy efficiency and attainable transmission rates. The results show that the performance of our semi-distributed approach is very close to that obtained with a pure optimal centralized one. [ABSTRACT FROM AUTHOR]

Transmit antenna selection (TAS) at base station has been widely studied and employed in many communication networks (such as those using massive multiple-input multiple-output (MIMO) systems). Thanks to the small size of microstrip antenna elements applicable to millimeter-wave (mmWave) frequencies, the implementation of TAS in small user equipments (UEs) employing switchable directional antennas is recently becoming popular. In this paper, we consider device-to-device (D2D) communications underlaying a cellular network wherein each D2D UE is equipped with switchable transmit antennas. By employing Generalized Bender’s Decomposition (GBD) algorithm, we obtained the solution to the problem of globally optimal transmit antenna selection and channel allocation to D2D UEs together with transmit powers of cellular and D2D UEs. Although we reformulated the non-convex primal subproblem of the proposed GBD-based method into convex form, we further managed to obtain the corresponding closed-form solution through analytical manipulations; this extensively increased (at least 60 times) the execution speed of the proposed method as shown in the simulation results. [ABSTRACT FROM AUTHOR]

With the emergence of 5G mobile multimedia services, end users' demand for high-speed, low-latency mobile communication network access is increasing. Among them, the device-to-device (D2D) communication is one of the considerable technology. In D2D communication, the data does not need to be relayed and forwarded by the base station, but under the control of the base station, a direct local link is allowed between two adjacent mobile devices. This flexible communicationmode reduces the processing bottlenecks and coverage blind spots of the base station, and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless networks. One of the important factors which affects the quality-of-service (QoS) of D2D communications is co-channel interference. In order to solve this problem of co-channel interference, this paper proposes a graph coloring based algorithm. The main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user resource. The proposed algorithm also provides simpler power control. The heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is acquired. Simulation results show that the proposed algorithm effectively reduced the co-channel interference, power consumption and improved the system throughput as compared with existing algorithms. [ABSTRACT FROM AUTHOR]

In this letter, we investigate a reconfigurable intelligent surfaces (RIS)-aided device to device (D2D) communication system over Rician fading channels with imperfect hardware including both hardware impairment at the transceivers and phase noise at the RISs. This letter has optimized the phase shift by a genetic algorithm (GA) method to maximize the achievable rate for the continuous phase shifts (CPSs) and discrete phase shifts (DPSs). We also consider the two special cases of no RIS hardware impairments (N-RIS-HWIs) and no transceiver hardware impairments (N-T-HWIs). We present closed-form expressions for the achievable rate of different cases and study the impact of hardware impairments on the communication quality. Finally, simulation results validate the analytic work. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is an emerging paradigm that can improve system capacity and spectral efficiency by using cooperative communication coexisting with cellular networks. In spite of these advantages, D2D communication suffers from unfair resource usage, security risks posed by eavesdroppers, and limited energy storage. To deal with these issues, in this paper, we propose a resource allocation algorithm to maximize the security-aware energy efficiency (EE) for D2D users (DUs) in a simultaneous wireless information and power transfer (SWIPT)-enabled D2D communication system with $\alpha$ fairness, where multiple random eavesdroppers are present. In particular, we formulate a multi-objective resource allocation problem by jointly optimizing the transmit power, power-splitting (PS) factors of DUs, and the sub-channel allocation factor under multiple constraints, including the maximum interference power for each cellular user, the maximum transmit power of each DU, the PS factor, and the integer sub-channel assignment. To solve the non-convex problem, an iterative algorithm is developed to obtain the sub-optimal solution. Simulation results verify that the proposed algorithm outperforms benchmark algorithms in terms of balancing secrecy EE and fairness. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication with short communication distance is an efficient way to improve spectrum efficiency and mitigate interference. To realize the optimal resource configuration including wireless channel matching and power allocation, a distributed resource matching scheme is proposed based on deep reinforcement learning(DRL). The reward is defined as the difference of achieve rate of D2D users and the consumed power, which is limited by the Signal to Interference plus Noise Ratio (SINR) of the other cellular users on the current channel. The proposed algorithm maximizes the D2D throughput and energy efficiency in a distributed manner, without online coordination and message exchange between users. The considered resource allocation problem is formulated as a random non-cooperative game with multiple players (D2D pairs), where each player is a learning agent, whose task is to learn its best strategy based on locally observed information, multi-user communication resource matching algorithm is proposed based on a Double Deep Q-network (DDQN), where the total cellular throughput and user energy efficiency could converge to the Nash equilibrium (NE) under the mixed strategy. Simulation results show that the proposed algorithm can improve the communication rate and energy efficiency of each user by selecting the optimal strategy, and has better convergence performance compared with existing schemes. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) in satellite communication (SATCOM) system can bring high spectral efficiency and massive connectivity. In this paper, we investigate files delivery and share optimization in LEO satellite-terrestrial integrated networks (STINs). A NOMA based coalition formation game (CFG) approach is proposed for minimizing total cost, in which satellite transfer files to head users in each group via NOMA, and head users utilize device to device (D2D) communications to share files among users. Firstly, head users selection algorithm is proposed to choose each group's head users receiving files from satellite. Precoding vectors and transmit power optimization with imperfect channel state information (CSI) are derived to achieve successful NOMA transmission and files download cost minimization in each group. A graph theory based algorithm is proposed to find the optimal D2D links and minimize files share cost. Then, we formulate a CFG and propose a preference order (Group-Best order) for user grouping. Furthermore, we have proved that the CFG with Group-Best order is an exact potential game (EPG), which can get stable group partition and achieve global optimization, i.e., minimizing total cost. Finally, a best response algorithm for NOMA based CFG (NCFG) is proposed to find stable group partition. Simulation results verify that our proposed approach is better than other approaches. [ABSTRACT FROM AUTHOR]

The physical layer security (PLS) of millimeter wave (mmWave) communication systems is investigated, where the secure source-to-destination communication is assisted by an untrusted relay selected from a group of them and there are also several passive eavesdroppers (Eves) in the network. In the considered system model, while the distributions of the untrusted relays and Eves follow a homogeneous Poisson Point Process (PPP). To maximize the instantaneous secrecy rate, a novel joint relay selection and power allocation (JRP) method is developed where the destination and source aim for jamming the reception of both the untrusted relays and passive Eves. New expressions of the optimal power allocation (OPA) are derived for both non-colluding Eves (NCE) and colluding Eves (CE). Subsequently, by considering the impact of potential blockages, new closed-form equations are derived for analyzing the system’s ergodic secrecy rate (ESR) and secrecy outage probability (SOP) for transmission over fading mmWave channels. Finally, numerical examples are provided for demonstrating the superiority of our proposed JRP method over the relevant benchmarks found in the literature. Interestingly, the ESR increases with the density of untrusted relays for both the NCE and CE scenarios, which is a benefit of the improved probability of selecting a relay with a stronger second-hop channel. Furthermore, in the low transmit power regime, employing relatively low mmWave frequencies achieves better ESR, while in the high transmit power regime, high mmWave frequencies provide higher ESR. [ABSTRACT FROM AUTHOR]

This letter investigates the ultra-reliable and low-latency link allocation for device-to-device (D2D) communications in face of sporadic packet arrivals. Specifically, contention-based access scheme has been widely used to achieve low-latency transmission, where the users can randomly access the pre-reserved resource pool without resource scheduling. However, considering the inconsistent packet arrivals and heterogeneous spatial distribution of D2D pairs (DPs), some DPs who are closely located or have more frequent packet arrivals are more likely to suffer collisions, which seriously deteriorates their reliability performance. In this context, the resource reservation can well offset the weaknesses of contention-based access scheme by reserving dedicated links to these DPs. Then, the hybrid link allocation issue is formulated as the optimization problem which aims to minimize the sum packet error probability under the low latency constraint. Moreover, an exact potential game model and a blocking-pair based best response algorithm are proposed. Simulation results verify the superiority of the proposed hybrid link allocation scheme. [ABSTRACT FROM AUTHOR]

Edge caching has become an effective solution to cope with the challenges brought by the massive content delivery in cellular networks. In device-to-device (D2D) enabled caching cellular networks with time-varying content popularity distribution and user terminal (UT) location, we model these dynamic networks as a stochastic game to design a cooperative cache placement policy. The cache placement reward of each UT is defined as the caching incentive minus the transmission power cost for content caching and sharing. We consider the long-term cache placement reward of all UTs in this stochastic game. In an effort to solve the stochastic game problem, we propose a multi-agent cooperative alternating Q-learning (CAQL) based cache placement algorithm. The caching control unit is defined to execute the proposed CAQL, in which, the cache placement policy of each UT is alternatively updated according to the stable policy of other UTs during the learning process, until the stable cache placement policy of all the UTs in the cell is obtained. We discuss the convergence and complexity of CAQL, which obtains the stable cache placement policy with low space complexity. Simulation results show that the proposed algorithm can effectively reduce the backhaul load and the average content access delay in dynamic networks. [ABSTRACT FROM AUTHOR]

Multicasting does not work well for video applications such as video on demand, where individual users request video content at discrete and unpredictable moments. In this paper, we study a concurrent video multicast orchestration solution for the caching-assisted and centrally controlled mobile networks. The proposed solution strives to obtain rapid video prefetch and effective traffic reduction. By rapid video prefetch, user devices can cache required contents in advance, which can effectively improve the streaming fluency in unsteady wireless environments, especially the highly mobile vehicular environment. We introduce a maximum-rate concurrent multicast (MRCM) problem whose solution is intended to maximize the total video delivery rate by a concurrent multicast manner, and prove that the MRCM problem is NP-hard. Then, we propose a dynamic concurrent multicast tree construction algorithm and a global concurrent multicast tree optimization algorithm to approximately solve the MRCM problem in polynomial time. We also introduce a cache competition mechanism that strives to maximize the total cache hit rate when the cache space is insufficient. Performance evaluations show that compared with the existing cache-assisted multicast delivery solutions, the proposed solution significantly reduces the video delivery duration and the traffic stress of backhaul links. [ABSTRACT FROM AUTHOR]

Due to the limited computation resources and lifetime of user equipment, we study the energy minimization problem for computation offloading in cognitive radio networks (CRNs). This work proposes a device-to-device (D2D)-assisted computation offloading scheme for non-orthogonal multiple access (NOMA)-enabled CRNs. Specifically, the secondary user (SU) can provide computation resources for the primary user (PU) to access the spectrum owned by the PU. With the constraints of task deadline and maximum transmit power, offloading decision and power control of PU and SU are optimized to minimize the energy consumption of CRNs. The solution is obtained by deploying the block coordinate descent method and successive convex approximation. Simulation results show the improvement of the proposed scheme in terms of energy consumption and computing performance compared with other methods. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is an emerging 5G technology to improve spectrum efficiency and reduce data traffic load in wireless networks. In this paper, a joint uplink-downlink resource allocation (JUDRA) approach is presented to maximize the network throughput while guaranteeing the quality of service (QoS) for both cellular users (CUs) and D2D users (DUs). In particular, the proposed JUDRA approach allows DUs to reuse either uplink or downlink CUs’ resources. Besides, an optimization problem is formulated as a mixed-integer nonlinear programming problem (MINLP) which is generally NP-hard. The problem is solved in two stages: sub-channel (SC) assignment stage and power allocation stage by utilizing the characteristics of sequential geometric programming. Moreover, a low computational complexity approach is suggested that converges to the near-optimal solution in polynomial time. Finally, comparative results are presented to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies the interplay between device-to-device (D2D) communications and real-time monitoring systems in a cellular-based Internet of Things (IoT) network. In particular, besides the possibility that the IoT devices communicate directly with each other in a D2D fashion, we consider that they frequently send time-sensitive information/status updates (about some underlying physical processes observed by them) to their nearest cellular base stations (BSs). Specifically, we model the locations of the IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another independent PPP. For this setup, we characterize the performance of D2D communications using the average network throughput metric whereas the performance of the real-time applications is quantified by the Age of Information (AoI) metric. The IoT devices are considered to employ a distance-proportional fractional power control scheme while sending status updates to their serving BSs. Hence, depending upon the maximum transmission power available, the IoT devices located within a certain distance from the BSs can only send status updates. This association strategy, in turn, forms the Johnson-Mehl (JM) tessellation, such that the IoT devices located in the JM cells are allowed to send status updates. The average network throughput is obtained by deriving the mean success probability for the D2D links. On the other hand, the temporal mean AoI of a given status update link can be treated as a random variable over space since its success delivery rate is a function of the interference field seen from its receiver. Thus, in order to capture the spatial disparity in the AoI performance, we characterize the spatial moments of the temporal mean AoI. In particular, we obtain these spatial moments by deriving the moments of both the conditional success probability and the conditional scheduling probability for status update links. Our results provide useful design guidelines on the efficient deployment of future massive IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications. [ABSTRACT FROM AUTHOR]

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL). To improve the communication efficiency of FL implementations in wireless systems, recent works have proposed compression and dimension reduction mechanisms, along with digital and analog transmission schemes that account for channel noise, fading, and interference. The prior art has mainly focused on star topologies consisting of distributed clients and a central server. In contrast, this paper studies FL over wireless device-to-device (D2D) networks by providing theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent (DSGD). First, we introduce generic digital and analog wireless implementations of communication-efficient DSGD algorithms, leveraging random linear coding (RLC) for compression and over-the-air computation (AirComp) for simultaneous analog transmissions. Next, under the assumptions of convexity and connectivity, we provide convergence bounds for both implementations. The results demonstrate the dependence of the optimality gap on the connectivity and on the signal-to-noise ratio (SNR) levels in the network. The analysis is corroborated by experiments on an image-classification task. [ABSTRACT FROM AUTHOR]

Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid federated learning (TT-HF), a semi-decentralized learning architecture that combines the conventional device-to-server communication paradigm for federated learning with device-to-device (D2D) communications for model training. In TT-HF, during each global aggregation interval, devices (i) perform multiple stochastic gradient descent iterations on their individual datasets, and (ii) aperiodically engage in consensus procedure of their model parameters through cooperative, distributed D2D communications within local clusters. With a new general definition of gradient diversity, we formally study the convergence behavior of TT-HF, resulting in new convergence bounds for distributed ML. We leverage our convergence bounds to develop an adaptive control algorithm that tunes the step size, D2D communication rounds, and global aggregation period of TT-HF over time to target a sublinear convergence rate of $\mathcal {O}(1/t)$ while minimizing network resource utilization. Our subsequent experiments demonstrate that TT-HF significantly outperforms the current art in federated learning in terms of model accuracy and/or network energy consumption in different scenarios where local device datasets exhibit statistical heterogeneity. Finally, our numerical evaluations demonstrate robustness against outages caused by fading channels, as well favorable performance with non-convex loss functions. [ABSTRACT FROM AUTHOR]

Efficient transmission protocols are required to minimize the energy consumption of mobile devices for ubiquitous connectivity in the next-generation of wireless networks. In this article, we analyze the energy consumption performance of a two-hop opportunistic device-select relaying (ODSR) scheme, where a device can either transmit data directly to a base station (BS) or relay the data to a nearby device, which forwards the data to the BS. We select a single device opportunistically from a device-to-device (D2D) network based on the energy required for transmission, including the energy consumed in the circuitry of the devices. By considering the log-normal shadowing as the dominant factor between devices and the BS, and Rayleigh fading in D2D links, we derive analytical bounds and scaling laws on average energy consumption. The derived analytical expressions show that the energy consumption of the ODSR decreases logarithmically with an increase in the number of devices, and achieves near-optimal performance only with a few nearby devices. This is an important design criterion to reduce latency and overhead energy consumption in a relay-assisted large-scale network. We also demonstrate the performance of the ODSR using simulations in realistic scenarios of a wireless network. [ABSTRACT FROM AUTHOR]

An integration of nonorthogonal multiple access (NOMA) in device-to-device (D2D) mobile groups (DMGs) generates a large amount of energy and delay due to sharing the same subcarrier. In this article, we investigate a distributed energy-efficient and delay tradeoff scheme for NOMA-based DMGs in wireless powered communication networks. The stochastic optimization problem is formulated to maximize the energy efficiency and minimize the delay. Many-to-one matching-based subcarrier allocation, weighted sum method, and Lyapunov optimization scheme are used to investigate the possible solution. Further, to obtain the closed-form expression of power and to optimize the time for wireless powered communication networks, we use Lagrangian function. Simulation results show that the proposed scheme achieves 2.6% and 15.78% superior performance than the existing NOMA and orthogonal frequency division multiple access (OFDMA) schemes under the condition of constant circuit power consumption. [ABSTRACT FROM AUTHOR]

The Device-to-device (D2D) networks bear a close resemblance to future Internet of Things (IoT) networks. Resource management is an important aspect for realization of D2D communication in upcoming IoT networks. In this article, we select underlay in-band D2D communication as it is more beneficial in terms of spectral efficiency even though it comes at the cost of interference with cellular communication. To deal with this difficulty, we propose a novel downlink resource sharing strategy, where multiple D2D users (D2Ds) share the resource of a single cellular user (CU), and each D2D pair reuses multiple channels from different cellular users (CUs). The proposed scheme adopts a channel selection technique, wherein multiple channels of CU can be shared by each D2D user. Furthermore, optimal power for each D2D user is determined through the Lagrangian dual optimization method. The formulated power control maximization scheme nicely balances the total transmission power of D2D and D2D sum-rate. The proposed channel and power allocation problem aims at maximizing the D2D sum-rate by increasing the number of active D2D links while preserving the quality of service (QoS) of CU. Finally, a relationship between energy efficiency (EE) and transmit power of D2D is investigated through an EE maximization problem. The overall system performance is evaluated in terms of the D2D shared ratio, throughput gain of the network, and computational complexity of the proposed optimal strategy. Further, the merits of using the proposed resource sharing scheme over the existing schemes are also verified through numerical results. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) band, i.e., 30 to 300 GHz, is characterized by short range transmissions and vulnerability to path blockage necessitating the use of relaying. Probing more relays finds out the best relay having the highest spectral efficiency but at the expense of increasing the probing overhead due to excessive beamforming training (BT) causing a decrease in the overall throughput. In this paper, mmWave two-hop relaying will be formulated as a single player multi-armed bandit (MAB) problem enabling one relay probing while maximizing the achievable spectral efficiency. Moreover, the relays could not establish the mmWave link due to blockage for instance will be identified as sleeping relays and eliminated from the rest of the MAB game. Thus, sleeping non contextual MAB (S-MAB) algorithm, namely sleeping Thompson sampling (S-TS) will be proposed to handle the problem. Furthermore, by utilizing the multiband capability of standardized WiGig devices containing both 2.4/5 GHz WiFi and 60 GHz mmWave bands, WiFi information will be used as contexts of the MAB game. Therefore, sleeping contextual MAB (S-CMAB) algorithm, namely S-CTS, will be proposed as well. Numerical and regret analysis ensure the superior performance of the S-CMAB algorithm over the S-MAB counterpart and the existing mmWave relay probing solutions accompanied with high convergence rates. [ABSTRACT FROM AUTHOR]

In this paper, we study an underlay device-to-device (D2D) communication system where the secondary D2D transmitter ($D_{Tx}$) communicates with D2D receiver ($D_{Rx}$) under ultra-reliable low-latency communication (URLLC) constraints. The $D_{Tx}$ attempts to send the mission-critical information to $D_{Rx}$ with fixed and proportional power constraints in order to prevent interference at the mobile primary cellular user (CU). We demonstrate the performance of the considered D2D URLLC system in terms of the block error rate and system goodput in the finite blocklength regime. Our analysis reveals the key insights related to the impact of mobility of primary CU, $D_{Tx}$ transmission power, blocklength, and packet size on the reliability and throughput performance of the cognitive network. The theoretical results are validated using Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

In this paper, a robust transmit power control scheme is proposed for multi-channel underlay device-to-device (D2D) communications with imperfect channel state information (CSI). The transmit power of the D2D user equipment (DUE) on each channel is optimized to maximize the average spectral efficiency (SE) whilst maintaining the quality-of-service (QoS) of the cellular user equipment (CUE) in the presence of errors in the CSI. To this end, we propose a novel deep neural network (DNN) structure and training methodology, in which artificially distorted CSI is used to compensate for the effect of imperfect CSI, such that a robust transmit power control strategy against channel error can be derived. Our simulation results show that even when the CSI is inaccurate, in our proposed scheme the degradation of the average SE can be kept low whilst maintaining negligible QoS violation, thereby confirming its effectiveness and robustness. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the content delivery in edge caching networks where requesters can get the interested contents from surrounding helpers by device-to-device (D2D) communications. Taking the finite service capacity and cache space of helpers into account, the problem is to maximize the number of requesters obtaining all desired contents by optimizing the selection of content sources. The hypergraph theory is first applied to analyze the cumulative content effect caused by multiple content sources, and the problem is formulated as a hypergraph based cooperation cache game with at least one pure Nash equilibrium (NE). The log-linear based cooperation caching algorithm (LBCC) is proposed to find the corresponding NE points. Simulation results verify the superiority of the proposed LBCC. [ABSTRACT FROM AUTHOR]

The capability of Non-Orthogonal Multiple Access (NOMA) for minimizing interference at the receiver using successive interference cancellation (SIC) makes it as a promising solution for 5G mobile communication. Device-to-device (D2D) communication is a part of the 5G network which provides frequency reuse in cellular networks and provides a better quality of service (QoS) to co-channel users. In this paper, we investigate the resource allocation and time scheduling algorithms for a NOMA based D2D communication system. In addition, to overcome the problem of energy at D2D transmitter and receivers, wireless powered communication network (WPCN) is used. Considering these issues, the objective of the proposed scheme is to maintain energy efficient communication among D2D transmitters and receivers without compromising energy efficiency (EE) of cellular users. To solve the fractional form of an optimization problem, we employ the Dinkelbach method. A many-to-many matching game approach is used for channel allocation and successive convex approximation for low complexity (SCALE) is used to control the power of the D2D transmitter. The simulation results demonstrate that by maximizing the EE and optimizing the time and transmission power of D2D groups, the energy efficiency of the network can be significantly improved in comparison to the other competitive schemes in the existing literature. [ABSTRACT FROM AUTHOR]

This paper proposes to deploy multiple reconfigurable intelligent surfaces (RISs) in device-to-device (D2D)-underlaid cellular systems. The uplink sum-rate of the system is maximized by jointly optimizing the transmit powers of the users, the pairing of the cellular users (CUs) and D2D links, the receive beamforming of the base station (BS), and the configuration of the RISs, subject to the power limits and quality-of-service (QoS) of the users. To address the non-convexity of this problem, we develop a new block coordinate descent (BCD) framework which decouples the D2D-CU pairing, power allocation and receive beamforming, from the configuration of the RISs. Specifically, we derive closed-form expressions for the power allocation and receive beamforming under any D2D-CU pairing, which facilitates interpreting the D2D-CU pairing as a bipartite graph matching solved using the Hungarian algorithm. We transform the configuration of the RISs into a quadratically constrained quadratic program (QCQP) with multiple quadratic constraints. A low-complexity algorithm, named Riemannian manifold-based alternating direction method of multipliers (RM-ADMM), is developed to decompose the QCQP into simpler QCQPs with a single constraint each, and solve them efficiently in a decentralized manner. Simulations show that the proposed algorithm can significantly improve the sum-rate of the D2D-underlaid system with a reduced complexity, as compared to its alternative based on semidefinite relaxation (SDR). [ABSTRACT FROM AUTHOR]

In this paper we study a data-supported caching policy design for wireless D2D caching networks, which is based on a dataset collected from a campus Wi-Fi network. After a well-designed preprocessing for the dataset, for the first time, we conduct a real dataset based performance evaluation for the caching policies designed based on the homogeneous Poisson Point Process (PPP) model and a clustered PPP model. We proceed to propose a novel approach for the design of the D2D caching policy. It directly models the number of D2D neighbours, instead of characterizing the locations of users as the PPP models. We show that the number of D2D neighbours can be well modeled by a discrete Gamma distribution. Given the model, we develop an iterative algorithm to optimize the D2D caching policy, and also provide a method to optimize the cache update time in order to balance the caching gain and overhead. Simulation results based on the dataset show that the proposed caching policy can achieve good performance with low cost of cache updating. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the overall network’s spectrum efficiency (SE) and energy efficiency (EE), respectively, by jointly optimizing the spectrum reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. To solve both mixed-integer non-linear programming problems, we first propose an efficient and low-complexity user-pairing scheme based on relative channel strength to determine the spectrum reuse indicators. Other variables are then optimized to maximize the SE by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. The EE-maximization problem is solved by an alternating algorithm integrated with Dinkelbach’s method. Numerical results show that the proposed design achieves significant SE and EE enhancements compared to traditional underlay D2D network without RIS, relay-assisted D2D network and other benchmarks. [ABSTRACT FROM AUTHOR]

In a 5G industrial IoT, using fog devices (FD) to release the burden of transmission data between a base station (BS) and a register user, is currently a promising method for device-to-device (D2D) communications. In this article, we propose a collaborative communication ecosystem to deploy RF-powered FDs as intermediaries to improve the utility as well as the fairness. We examine three contributing works that activate a relay-labor exchanging market. First, we build Stackelberg games to attract D2D collaborations; second, we employ an energy harvest technology and a time-switching power scheme to make exchange benefits to support the games; third, to reduce the relay redundancy and guarantee effective transmission rates, we employ network-coded flows. To solve the double-objects optimization model of our collaborative games, a genetic algorithm NSGA-II is applied to find an optimal solution to the market. We also mathematically prove the existence of an equilibrium state in the proposed games, and afterwards simulate the whole scenario using Mininet for a performance analysis. The simulation results in Mininet show that the BS and FDs in the market games all achieve desirable utilities as well as fairness among FDs. [ABSTRACT FROM AUTHOR]

In post disaster scenarios such as war zones floods and earthquakes, the cellular communication infrastructure can be lost or severely damaged. In such emergency situations, remaining in contact with other rescue response teams in order to provide inputs for both headquarters and disaster survivors becomes very necessary. Therefore, in this research work, a design, implementation and evaluation of energy aware rapidly deployable system named EA-RDSP is proposed. The proposed research work assists the early rescue workers and victims to transmit their location information towards the remotely located servers. In EA-RDSP, two algorithms are proposed i.e., Hop count Assignment (HCA) algorithm and Maximum Neighbor Selection (MNS) algorithm. The EA-RDSP contains three types of nodes; the client node sends information about casualty in the disaster area to the server, the relay nodes transmit this information from client node to server nodes via multi-hop transmission, the server node receives messages sent by client node to alert rescue teams. The EAM-RDSP contains three types of nodes; the client node sends information about casualty in the disaster area to the server, the relay nodes transmit this information from client node to server nodes via multi-hop transmission, the server node receives messages sent by client node to alert rescue teams. The proposed EA-RDSP scheme is simulated using NS-2 simulator and its performance is compared with existing scheme in terms of end-to-end delay, message delivery ratio, network overhead and energy consumption. [ABSTRACT FROM AUTHOR]

Mobile storage systems (MSSs) which store popular content in mobile devices can reduce the heavy traffic burden on the base station (BS). To deal with data loss caused by the mobility of devices, erasure codes are widely used in practice to improve system reliability. The duration to repair all lost data is defined the repair delay. However, the repair delay via device-to-device communications is usually large due to intermittent contacts among devices within a given communication range, which causes files to be irreparable and permanently lost. This paper focuses on analyzing the repair delay of the MSSs through D2D communications. We adopt a coded repair process running periodically and derive analytical expressions of the average repair delay by taking into account factors, i.e., limited communication range, device mobility, and coded repair scheme. Moreover, considering that devices need multipath contacts to repair lost data for large files, we propose a relay cooperation repair scheme, in which some mobile devices can act as relay nodes to transmit data cooperatively. Furthermore, we design a heuristic algorithm to optimize the number of relay devices and the amount of data allocated to each path, with the objective of minimizing the average repair delay. We find that maximum distance separable codes can yield smaller average repair delays for small files, and the average repair delay can be reduced by selecting faster moving devices to participate. With the file size increasing, the relay cooperation repair scheme can significantly improve repair efficiency in MSSs using the proposed heuristic algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we consider a coexisting network of cellular transmission and device-to-device (D2D) communication, in which two users need to receive downlink signals from base station (BS), meanwhile, they want to exchange information with each other. For such a network, a hybrid cellular and bidirectional D2D cooperative non-orthogonal multiple access (HCBD-CNOMA) scheme is proposed. To characterize the performance of our proposed scheme, we derive the closed-form ergodic rate and outage probability by considering both perfect and imperfect successive interference cancellation (SIC). To acquire more insights, their corresponding asymptotic expressions are obtained as well. Moreover, considering the fact that the nodes in Internet of Things (IoT) network are usually powered by battery and thus their power is limited, we further consider the scenario that two users are energy-constraint. In this scenario, users first need to harvest energy from the transmission of BS based on time splitting (TS) or power splitting (PS) energy harvesting (EH) protocol and then use these energy to accomplish subsequent communications. Similarly, the exact ergodic rate and outage probability as well as their asymptotic expressions for this scenario are analyzed. Simulation results validate the accuracy of analytical results, and reveal the performance gain of our schemes over the benchmark schemes. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication affords the benefits of improved network spectral efficiency, throughput, energy efficiency, and delay performance. However, D2D communication underlaid cellular uplinks are degraded by interference between D2D user equipment (DUE) and cellular user equipment (CUE) that use the same frequency band. In this context, researchers have proposed the optimization of the transmit power of the devices to mitigate interference. This power allocation problem is generally modeled as a NP-hard combinatorial optimization problem with linear constraints; therefore, traditional optimization methods are ineffective in addressing the problem. In this paper, we apply a deep reinforcement learning (DRL) algorithm to optimize the transmit power for both DUEs and CUEs in the context of D2D communication underlaid uplink cellular networks, which can solve the optimization problem via optimal decision-making together with efficient deep network training. Our simulation results show that the proposed algorithm affords a near-global-optimum solution with lower computation complexity than exhaustive search, and outperforms the fixed-power, weighted minimum mean square error (WMMSE), gradient descent method, and Newton-Raphson approaches. [ABSTRACT FROM AUTHOR]

Multi-source device-to-device (D2D) communication allows the base station (BS) to serve the content requests of the users locally through D2D links. As a result, the load on the base stations (BSs) and the consumption of radio resources reduces significantly. Clearly, the success of multi-source content delivery relies on the willingness of the content owners (COs), i.e., sources, to deliver their content to requesting user. Consequently, in this article, we investigate the economic interaction between single BS and multiple COs for content delivery. In view of the fact that participating COs are heterogeneous with respect to the amount of content in their cache and the sensitivity towards energy consumption, there is a need to design a fair incentive mechanism which motivates COs for content delivery. To this end, we model the interaction among the BS and multiple COs as one-to-many bargaining game and design an incentive mechanism based on the Nash bargaining framework. Specifically, we obtain the optimal amount of content delivered by participating COs and their corresponding incentives under two variants of one-to-many bargaining, namely sequential bargaining and concurrent bargaining. For both variants of bargaining, the obtained optimal solutions are capable of minimizing the amount of content delivered by the BS while ensuring fair incentive transfer among the participating COs. [ABSTRACT FROM AUTHOR]

This paper provides a deep analysis for millimeter-wave device-to-device (mm-wave D2D) underlaid cellular networks to investigate how the performance of individual users or link percentiles under constraints are affected by the interaction between D2D and cellular communication modes integrated with the salient mm-wave features. Under a general Nakagami fading model, we derive the upper bounds for the $b$ -moments of the conditional signal-to-interference-noise ratio (SINR) distributions for the two modes given the network realization, and further calculate the upper bounds of SINR meta distributions (MDs). Moreover, we extend the MD-based analysis to evaluate the fine-grained performance for the transmission effectiveness, through deriving the bounding results for the user-perceived rate MD and spatial rate capacity (SRC), the latter of which is defined as the effective density of users satisfying a given transmission rate requirement with a certain probability. Numerical results validate the tightness of the proposed bounds and reveal the different effects of the network configuration parameter associated with mm-wave on the link and network performances from the view of individual users. [ABSTRACT FROM AUTHOR]

Edge side caching assisted device-to-device (D2D) communication has been acknowledged as a promising technique to alleviate the heavy burden of backhaul transmission link and to reduce the network latency. However, the effectiveness of caching strategies at the network edge is highly dependent on the distribution of individual user’s content preference. To fully attain the benefits of edge caching, some proactive mechanisms shall be considered. Among which, recommendation performs noticeably well due to its capability of reshaping the content request probabilities of different users, which in turn affects the cache decision significantly. In this work, we quantitatively investigate how recommendation can be applied to enhance the caching efficiency of D2D enabled wireless content caching networks. And for that, the cache hit ratio maximization problem for a generic network model is formulated taking into account the requirements of each user’s personalized recommendation quality, recommendation quantity and cache capacity. Then, we show that the optimal recommendation and caching policies which jointly maximize the cache efficiency is NP-hard to compute. Further, a time-efficient sub-optimal algorithm is designed, which works in an iterative manner and has provable convergence guarantee as well as polynomial time complexity. Monte-Carlo simulation results demonstrate the convergence performance of our proposed joint decision algorithm and its cache efficiency improvements compared to extensive benchmarks. [ABSTRACT FROM AUTHOR]

The rapid advancement in information and communication technology has revolutionized military departments and their operations. This advancement also gave birth to the idea of the Internet of Battlefield Things (IoBT). The IoBT refers to the fusion of the Internet of Things (IoT) with military operations on the battlefield. Various IoBT-based frameworks have been developed for the military. Nonetheless, many of these frameworks fail to maintain a high Quality of Service (QoS) due to the demanding and critical nature of IoBT. This study makes the use of mist computing while leveraging machine learning. Mist computing places computational capabilities on the edge itself (mist nodes), e.g., on end devices,wearables, sensors, andmicro-controllers. This way, mist computing not only decreases latency but also saves power consumption and bandwidth as well by eliminating the need to communicate all data acquired, produced, or sensed. A mist-based version of the IoTNetWar framework is also proposed in this study. The mist-based IoTNetWar framework is a four-layer structure that aims at decreasing latency while maintaining QoS. Additionally, to further minimize delays, mist nodes utilize machine learning. Specifically, they use the delay-based K nearest neighbour algorithm for device-to-device communication purposes. The primary research objective of this work is to develop a system that is not only energy, time, and bandwidth-efficient, but it also helps military organizations with time-critical and resources-critical scenarios to monitor troops. By doing so, the system improves the overall decision-making process in a military campaign or battle. The proposed work is evaluated with the help of simulations in the EdgeCloudSim. The obtained results indicate that the proposed framework can achieve decreased network latency of 0.01 s and failure rate of 0.25% on average while maintaining high QoS in comparison to existing solutions. [ABSTRACT FROM AUTHOR]

In task offloading systems, it is imperative to guarantee that an offloaded task is completed within a pre-specified deadline. In this paper, we propose a distributed device-to-device (D2D) offloading system (DDOS) in which a task owner opportunistically broadcasts an offloading request that includes its mobility level and task completion deadline. After receiving the request, mobile devices in the vicinity of the task owner employ a constraint stochastic game to decide, in a distributed manner, whether to accept the request or not. We devise a best response dynamics-based algorithm (BRDA) to obtain a multi-policy constrained Nash equilibrium. Evaluation results demonstrate that DDOS can guarantee a high on-time task completion probability, as well as a low energy consumption. [ABSTRACT FROM AUTHOR]

Unmanned Ariel Vehicles (UAVs) are tasked to collect sensory data which are typically retrieved after the flight. The emergence of 5G and Device-to-Device (D2D) networks enables high speed network communication for UAVs to transfer data during a flight mission instead of post flight. UAVs are now subject to constraints of area coverage, battery capacity and network quality of service, making their path planning more challenging. In this paper, we formulate the issue as a combinatorial optimization problem which minimizes the flight cost of multiple UAVs covering the entire area. We show this problem is NP-hard, therefore we propose a Particle Swarm Optimization heuristic along with path encoding and local search techniques to solve the problem. Our numerical simulations demonstrate the effectiveness of the approach and how the size of the area and D2D link affect the number of UAVs needed and their flight time.

Recommender systems recommend new movies, music, restaurants, etc. Typically, service providers organize such systems in a centralized way, holding all the data. Biases in the recommender systems are not transparent to the user and lock-in effects might make it inconvenient for the user to switch providers. In this paper, we present the concept, design, and implementation of MobRec, a mobile platform that decentralizes the data collection, data storage, and recommendation process. MobRec's architecture does not need any backend and solely consists of the users' smartphones, which already contain the users' preferences and ratings. Being in proximity in public places or public transportation, data is exchanged in a device-to-device manner, building local databases that can recommend new items. One of biggest challenges of such a system is the implementation of unobtrusive device-to-device data exchange on off-the-shelf Android devices and iPhones. MobRec facilitates such data exchange, building on Google Nearby Messages with Bluetooth Low Energy. We achieve the successful exchange of data within 3 to 4 minutes, making it suitable for the described scenario. We demonstrate the feasibility of decentralized recommender systems and provide blueprints for the development of seamless multi-platform device-to-device communication.

The concentrated popularity distribution of video files and the caching of popular files on users and their subsequent distribution via device-to-device (D2D) communications have dramatically increased the throughput of wireless video networks. However, since popularity distribution is not time-invariant, and the files available in the neighborhood can change when other users move into and out of the neighborhood, there is a need for replacement of cache content. In this work, we propose a practical and feasible replacement architecture for base station (BS) assisted wireless D2D caching networks by exploiting the broadcasting of the BS. Based on the proposed architecture, we formulate a caching content replacement problem, with the goal of maximizing the time-average service rate under the cost constraint and queue stability. We combine the reward-to-go concept and the drift-plus-penalty methodology to develop a solution framework for the problem at hand. To realize the solution framework, two algorithms are proposed. The first algorithm is simple, but exploits only the historical record. The second algorithm can exploit both the historical record and future information, but is complex. Our simulation results indicate that when dynamics exist, the systems exploiting the proposed designs can outperform the systems using a static policy.

Small cells (SCs) offer a promising approach to meeting the exponentially growing data rate demands. However, dense deployment of SCs can degrade the energy-efficiency (EE) of the network due to the additional deployed base stations (BSs). Under heterogeneous network (HetNet) deployments, SCs can be dynamically switched off for energy saving when traffic load decreases. We do this by defining a load-dependent transmission power coefficient (TPC) for SC BSs. In addition, with Device-to-Device (D2D) communication, mobile users in proximity can establish a direct link and bypass the BSs, thereby offloading the network infrastructure and providing further improvement of EE. In this paper, the objective is to rely on both D2D communications and sleeping SC BSs to offload traffic from the mains powered macrocell BSs leading to energy saving in the network. Furthermore, in order to consider D2D requirements under the practical application scenarios, we assume a framework for wireless video sharing, where users can store popular video files and share files via D2D communication. To begin with, we derive the EE expression of the cache-enabled D2D-aided HetNet. Then, to maximize the EE of the cache-enabled D2D-aided HetNet, the optimal load-dependent TPC for SC BSs is obtained under constraints on both network's coverage and rate. Using simulations, we will investigate the potential effects of the TPC defined for SC BSs as well as the introduced D2D layer on the network EE.