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This paper proposes on-driving experience sharing algorithms at junctions in infrastructure-assisted vehicles-to-everything networks. For the purpose, a millimeter-wave (mmWave) technology is used because it provides multi-Gbps data rates which is helpful for handling users’ short stay times at junctions and spatial reuse due to high beam directionality which is helpful for interference-avoidance among densely deployed vehicles at junctions. To realize on-driving experience sharing, the proposed algorithms focus on joint resource allocation and scheduling for 3GPP-compliant multiple unicast vehicle-to-vehicle (V2V) communications where the vehicles are group leaders (GLs) in 3GPP Mode 4(d). The resource allocation stands for the roadside unit (RSU) allocation to scheduled V2V GL links where RSU is essentially required for overcoming blockage by establishing two-hop relaying. Because vehicles stay for short times at junctions, this paper designs two algorithms without or with delay considerations. Without delay considerations, the joint optimization of RSU allocation and scheduling was originally formulated as mixed 0-1 non-convex optimization. However our proposed algorithm reformulates the problem into mixed 0-1 convex optimization, which is computationally easier to solve. With delay considerations, our proposed algorithm dynamically controls video contents frame rates for time-average on-driving video sharing quality maximization subject to delay constraints, inspired by Lyapunov optimization. Extensive simulation results demonstrate that our algorithms can significantly outperform in a variety of scenarios. Furthermore, we conduct the cost analysis for the proposed algorithms in terms of capital expenditure (CAPEX) and operating expenditure (OPEX). [ABSTRACT FROM AUTHOR]

In this paper, we investigate a two-hop simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay communication system with an energy-limited relay node. The relay node harvests energy based on the radio-frequency (RF) signal transmitted from the source node through the time-switching (TS) protocol and fully uses the harvested energy to precode and forward the information to the destination node. The non-linear energy harvesting models are considered at the relay node. With the consideration of the channel estimation error, the joint optimization of the TS factor, source and relay precoding matrices is proposed with robustness against the channel state information (CSI) mismatch to maximize the mutual information (MI) between the source and destination nodes. The optimal structure for the source and relay precoding matrices is derived to simplify the transceiver optimization problem. Numerical simulations show that the system performance provided by the proposed algorithms with robustness is better than the non-robust algorithm. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) play a critical role for 5 G/B5G communications. In this paper, we investigate the physical layer security issue for ground transmissions with the help of a UAV. Specifically, we consider the scenario without direct transmissions and employ the UAV as a mobile relay to amplify and forward the confidential information. Meanwhile, the receiver transmits cooperative jamming attempting to disrupt eavesdropping. We investigate the UAV deployment and receiver jamming strategy jointly for the optimized secrecy rate, while considering the channel uncertainties. In particular, for the case of complete channel state information (CSI), the jamming power is obtained by bi-section search, and we propose a data-trained deep neural network (DNN) to approximate the optimal UAV deployment. Meanwhile, for the case with partial CSI, we design a deep Q network (DQN) to learn the optimal UAV deployment, due to the increased complexity to determine the jamming strategy with channel statistics. Simulation results demonstrate that both the learned DQN and trained DNN model effectively approximate the optimal deployment of the UAV. Moreover, compared with the baselines, our proposal significantly improves the security performance of legitimate transmissions. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. In this paper, we present a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments (UEs) towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential eavesdropper called the untrusted UAV-relay (UUR). To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we devise an optimization framework in terms of a new measure $-$ secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UUR’s inherent flight power limitation into account. For the sake of quality of service fairness amongst all the UEs, we aim to maximize the minimum SEE (MSEE) performance via the joint design of key system parameters, including UUR’s trajectory and velocity, communication scheduling, and network power allocation. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we decouple it into four subproblems and propose alternative algorithms to solve it efficiently via greedy/sequential block successive convex approximation and non-linear fractional programming techniques. Numerical results demonstrate significant MSEE performance improvement of our designs compared to other known benchmarks. [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 consider the secure transmission problem in an unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) assisted mmWave networks in the presence of an eavesdropper. The UAV base station (UAV-BS) and IRS are deployed to overcome the blockages. Artificial noise (AN) is exploited against the eavesdropper. Aiming to maximize the secrecy rate, we jointly design the positions and beamforming of UAV-BS and IRS, where the positions here represent the deployed position of the UAV-BS and the activation position of the IRS. Meanwhile, the maximum transmits power, minimum height constraint for UAV-BS, and the legitimate receiver minimum rate constraint are required. To obtain the optimal UAV-BS position, the elevation angle-dependent probabilistic LoS air-to-ground channel is exploited in this network. To tackle the formulated non-convex problem, we divide it into two subproblems: (1) design UAV-BS and IRS positions; (2) design UAV-BS and IRS beamforming. Moreover, to reduce the complexity, we propose an ideal beamforming model to find the near-optimal positions of UAV-BS and IRS. Then, semidefinite relaxation (SDR) is utilized to cope with the highly coupled and high-dimensional variable vectors. Finally, we obtain a sub-optimal solution by an efficient and low-complexity alternating optimization algorithm. Numerical results demonstrate the improvement of secrecy rate by jointly designing the positions as well as the significant performance gains achieved by our proposed schemes over benchmark schemes. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access based cooperative relaying system (NOMA-CRS) has been proposed to alleviate the decay in spectral efficiency of the conventional CRS. However, existing NOMA-CRS studies assume perfect successive interference canceler at the relay and mostly investigate sum rate whereas the error performance has not been taken into consideration. In this paper, we analyze error performance of the NOMA-CRS and the closed-form bit error probability (BEP) expression is derived over Nakagami-m fading channels. Then, thanks to the high performance of machine learning (ML) in challenging optimization problems, a joint power sharing-power allocation (PS-PA) scheme is proposed to minimize the bit error rate (BER) of the NOMA-CRS. The proposed ML-assisted optimization has a very low online implementation complexity. Based on provided extensive simulations, theoretical BEP analysis is validated. Besides, the proposed ML-aided PS-PA provides minimum BER (MBER) and outperforms previous PA strategies for the NOMA-CRS notably. [ABSTRACT FROM AUTHOR]

This paper analyzes the problem of securing the transmissions of multiple mobile users against eavesdropping attacks. We propose and optimize the deployment of a single unmanned aerial vehicle (UAV), which serves as an aerial relay (AR) between the user cluster and the base station. The focus is on maximizing the secrecy energy efficiency by jointly optimizing the uplink transmission powers of the ground users and the position of the UAV. The joint optimization problem is nonconvex; therefore we split it into two subproblems and solve them using an iterative algorithm. [ABSTRACT FROM AUTHOR]

This paper studies a novel design of user pairing and resource allocation in a simultaneous wireless information and power transfer (SWIPT)-enabled cooperative non-orthogonal multiple access (NOMA) system with coexisting power-splitting (PS) and time-switching (TS) users. In this system, near NOMA users (NUs) that are close to a source can assist the communications between the source and far NOMA users (FUs) using the energy harvested by either PS or TS strategy. The design aims to maximize the sum-rate of the system by optimizing user pairing and power allocation between NUs and FUs as well as performing PS and TS control at NUs, while guaranteeing the target data rates and the minimum harvested energy requirements of users. Since the joint design is formulated as a non-convex mixed-integer non-linear programming (MINLP) problem that is challenging to tackle, we propose a two-step user pairing and resource allocation algorithm. Specifically, we decouple the original problem into an inner resource allocation problem and an outer user pairing problem. For the inner problem, we propose successive convex approximation (SCA) and block coordinate descent (BCD)-based algorithms to perform power allocation as well as PS and TS control iteratively, with the algorithms’ convergence being theoretically proved. By exploiting the characteristics of the outer problem of user pairing, this problem is well tackled by the Hungarian algorithm. Simulation results demonstrate the proposed joint design yields better performance gains than the existing schemes. [ABSTRACT FROM AUTHOR]

In this paper, we exploit the cooperative transmission as a new energy supply to support backscatter communication (BackCom), where there are multiple decode-and-forward (DF) relays and only one relay is selected in the cooperative system. The tag circuit power and signal backscattering are respectively supported by transmit powers of the source and the relay, and the minimum mean square error (MMSE) detector is employed at the receiver of the BackCom user to mitigate the interference from the cooperative system. Our objective is to maximize the quality of service (QoS) of the BackCom user which satisfying the QoS of the cooperative system, the total power constraint on the cooperative system and the harvested power constraint on the BackCom user. The probability of feasibility conditions, and optimized joint relay selection and power allocation are investigated in closed-form expressions. The zero forcing (ZF) detection and its comparison with the MMSE detection in the considered system are also analyzed. Simulation results demonstrate the benefit of the proposed scheme compared with traditional ones. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of cooperative networks based on non-orthogonal multiple access (NOMA) with multiple full-duplex decode-and-forward relays. Taking into account rate fairness between the users and assuming imperfect successive interference cancellation (SIC), the problem is formulated so that power allocation is performed adaptively by the multiple relays in order to maximize the minimum users’ achievable rate. We demonstrate that the proposed optimization problem is convex and a closed-form expression for the power allocation factor is derived. The attained results show that the proposed scheme achieves satisfactory results in terms of achievable rate, fairness, and outage probability when compared to other adaptive power allocation strategies. [ABSTRACT FROM AUTHOR]

This paper considers a cooperative cognitive radio network with two primary users (PUs), and two secondary users (SUs) that enables two-way communications of primary, and secondary systems in conjunction with non-linear energy harvesting based simultaneous wireless information, and power transfer (SWIPT). With the considered network, SUs are able to realize their communications over the licensed spectrum while extending relay assistance to the PUs. The overall bidirectional end-to-end transmission takes place in four phases, which include both energy harvesting (EH), and information transfer. A non-linear energy harvester with a hybrid SWIPT scheme is adopted in which both power-splitting, and time-switching EH techniques are used. The SUs aid in relay cooperation by performing an amplify-and-forward operation, whereas selection combining technique is adopted at the PUs to extract the intended signal from multiple received signals broadcasted by the SUs. Accurate outage probability expressions for the primary, and secondary links are derived under the Nakagami- $m$ fading environment. Further, the system behavior is analyzed with respect to achievable system throughput, and energy efficiency. Since the performance of the considered system is strongly affected by the spectrum sharing factor, and hybrid SWIPT parameters, particle swarm optimization is implemented to optimize the system parameters so as to maximize the system throughput, and energy efficiency. Simulation results are provided to corroborate the performance analysis, and give useful insights into the system behavior concerning various system/channel parameters. [ABSTRACT FROM AUTHOR]

In this paper, we study the network of unmanned aerial vehicles (UAVs) which acts as a relay between a base station, and remotely deployed Internet of Things devices. UAVs have received much attention for connecting remotely located networks/objects, but there is a lack of study on multi-hop relay transmission schemes over UAV networks. It is particularly challenging because of the their high mobility, and limited resources, to name a few. In this regard, we propose an energy-efficient, cooperative multi-hop relay scheme for UAVs. We formulate the optimal multi-hop transmission scheduling problem to minimizes the power consumption of UAVs without violating the given time constraint. We convert the initial formulation into a low-complex iterative algorithm, making it suitable for an online scheduling. Also, we propose a buffer prediction, and correction scheme to initiate the multi-hop transmission scheduling without having the perfect buffered data information. Evaluation results show that the proposed scheme can energy-efficiently schedule the multi-hop transmissions over dynamic UAVs without violating the time constraints. [ABSTRACT FROM AUTHOR]

This paper addresses the formulation of power allocation for cluster based multihop vehicular relaying communications in order to improve the network quality-of-services (QoS). The cluster-to-cluster (C2C) channel is completely depend on the power gain of vehicle-to-vehicle (V2V) channel that increases the difficulties of the power allocation problem. To meet this goal, we have derived a global channel obtained by joint V2V and C2C cooperation. The most challenging aspect is however the joint V2V and C2C power allocation of the global channel due to the association of its multi-variables objective function. To solve this problem, we apply an alternative optimization technique to make the global problem into a series of sub-problems. We then apply a semi-definite programming (SDP)-based iterative gradient descent (SDP-IGD) power allocation to assign power in each relay. The SDP-IGD power allocation algorithm has been extended from Lagrange Multiplier (LM) that provides better performance than separate LM technique. The minimum mean-square error (MMSE)-decision feedback equalizer (MMSE-DFE) is designed at the receiver to improve the signal detection capability under exact channel state information (CSI) in all forward-and-backward links. Finally, simulation results confirm that our proposed solution performs significantly superior compared to other solutions found in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we study a dual-hop wireless powered communication network (DH-WPCN), where the two-way communication between the users and the access point (AP) is assisted by a relay. Assuming a piece-wise linear energy harvesting (EH) model for the users and the relay, we investigate the problem of total throughput maximization for both amplify-and-forward (AF) and decode-and-forward (DF) relaying modes. To deal with the non-convexity of the throughput maximization problem, we apply the block coordinate descent (BCD) technique and alternately optimize different blocks of optimization variables until convergence is achieved. Numerical results demonstrate the substantial performance gain of the proposed scheme with piece-wise linear EH model as compared to the case with oversimplified linear EH model. This is due to the fact that our proposed scheme takes into consideration the saturation effect of practical EH circuits in optimizing the resource allocation. [ABSTRACT FROM AUTHOR]

We all have encountered unsatisfactory video viewing experience with mobile devices in hot spot areas where a crowd of people are demanding video streaming simultaneously. One possible solution to relieve the problem is to use the unmanned aerial vehicle (UAV) as a relay to improve the quality-of-experience (QoE). In this paper, we focus on the allocation of bandwidth and transmission power for multiuser video streaming in UAV relay networks. Our goal is to maximize the total long-term QoE of users, which are determined by the freezing time and the bit rate of video. We employ the Lyapunov optimization theory to transform the original problem with long-term average form into the drift-plus-penalty problem, and propose a decentralized allocation scheme using alternative ascending clock auction to derive the optimal solution. Simulations are conducted to demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal power allocation for a radio frequency (RF)-powered cooperative network and propose energy-saving time switching relaying (ES-TSR) and energy-saving power splitting relaying (ES-PSR) protocols with an iterative energy-saving (ES) algorithm. In particular, we develop the analytical expressions of the channel capacity and the total energy consumption for the considered cooperative network. We formulate the system energy efficiency maximization problems and then provide the corresponding solutions by deriving the source's optimal transmit power in terms of energy fraction consumed with time switching (TS) factor and power splitting (PS) factor for the proposed ES-TSR and ES-PSR protocols, respectively, in each transmission block. From the past approaches, the optimal power allocation to maximize the system energy efficiency of the RF-powered cooperative networks has been studied under the assumption that the relay must utilize all of its harvested power in each transmission block. This work considers optimal power allocation to maximize the system energy efficiency for the case when the relay node can save energy for the next transmission block. Our simulation results show that the proposed ES-TSR and ES-PSR protocols outperformed the existing TS-based and PS-based relaying protocols with $\text{14.22}\%$ and $\text{8.4}\% $ , respectively, in paticular when the number of transmission blocks $\boldsymbol{L} = 10$. [ABSTRACT FROM AUTHOR]

In this paper, we study unmanned aerial vehicle (UAV) aided internet of things (IoT) networks, where UAVs facilitate data transmission of IoT devices. We focus on uplink transmission from IoT devices to base station (BS). Multiple UAVs are employed as UAV relays between IoT devices and BS to enhance received signal strength at BS. Specifically, IoT devices periodically detect wireless channel quality between IoT devices and BS, as well as that among IoT devices. Based on the wireless channel quality, we propose a distributed user cluster (UC) algorithm to cluster IoT devices as multiple UCs. One IoT device in a UC, which is named cluster head (CH), is selected to connect to the BS and gather uplink signals of IoT devices. If the wireless channel quality between CH and BS is good, a direct connection between CH and the BS can be built. Otherwise, UAVs are divided into multiple UAV cooperative relay clusters (CRCs). The UAVs in a CRC are located between a specific CH and BS to relay uplink signals. We then formulate a system optimization model to minimize system power consumption, where UAV deployment and transmission power of UAV are jointly optimized. We solve this optimization problem by dual decomposition method. By extensive simulations, we demonstrate the effectiveness of the proposed algorithm. We also reveal several interesting insights for practical UAV aided IoT networks. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a UAV-assisted cooperative communication system based on simultaneous wireless information and power transfer (SWIPT), where the UAV serves as a relay and its transmission capability is partly powered by radio signal from the source via the time-sharing mechanism. We study the end-to-end cooperative throughput maximization problem by optimizing the UAV's decision profile, power profile and trajectory for both amplify-and-forward (AF) and decode-and-forward (DF) protocols. The problem is decomposed into three optimization subproblems for decision profile, power profile and trajectory, and solved through alternating optimization, by which each of the subproblems is solved with the other two fixed. A binarization algorithm is further proposed to make the decision profile feasible. We show that the proposed solution outperforms not only two SWIPT-based strategies, but also a similar solution from an existing work without consideration for SWIPT. In addition, results indicate that the proposed algorithm performs efficiently in both optimality and convergence. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-way cooperative relay scheme based on non-orthogonal multiple access (NOMA) where bidirectional communication is supported for two users in three phases. We jointly optimize power and time allocations to minimize system outage probability whose exact closed-form expression is derived and verified by Monte Carlo simulations. For performance comparison, four schemes based on power allocation and time allocation as well as the conventional one-way cooperative NOMA (OW-CNOMA) scheme are provided. It is shown that the proposed scheme can improve outage probability and ergodic rate performances over a conventional OW-CNOMA system. [ABSTRACT FROM AUTHOR]

Mobility-tolerant data forwarding over time-varying IoT networks is a challenging problem. Selection of a relay node at every instant in time is crucial and calls for robust and distributed methods to improve the quality of services in such networks. In this paper, an optimal relay node selection method is proposed for robust data forwarding in time-varying networks. The proposed method selects a relay node based on joint optimization of two network parameters, namely data latency and link reliability. The Pareto front of this multi-objective optimization problem is used to illustrate the tradeoff between low data latency and high link reliability. The proposed optimal relay node selection method first identifies all possible relay nodes at every time instant. The weights of the bi-objective problem are updated at every hop and an optimal relay node is selected among all the possible relay nodes, by solving the joint optimization problem. During performance evaluation, random spatial patterns of Internet of Things (IoT) devices are modeled using the homogeneous Poisson point processes. The connectivity duration information of all the IoT devices with their neighbors is updated continuously and a relay node found is in an online manner. Simulation results indicate that the proposed method significantly improves data latency and the packet loss risk for data forwarding over time-varying IoT networks, when compared to conventional single parameter optimization methods. The proposed data forwarding method indicates additional gains in terms of transmission range, mobility tolerance, packet replication cost, and connectivity parameters of the time-varying IoT network. [ABSTRACT FROM AUTHOR]

In this paper, a new cooperative two-way cognitive relaying scheme is proposed. In this model, a primary user (PU) network consisting of two PU sources communicate with each other via a single amplify-and-forward (AF) relay. In addition, a secondary user (SU) source transmits its data to an SU destination via the same PU relay node. To mitigate the SU interference caused to the PU network, the PU network considers the SU network pairs as two additional relay nodes helping the original PU relay node in improving the PU network performance. As a reward for its cooperation, the PU network allows the SU network to communicate simultaneously via the PU relay node by using the decode-and-forward protocol. The proposed system allows the transmission of four PU symbols and one SU symbol in four/three time slots resulting in a bandwidth efficiency of $1.25/1.67$ based on the cooperative scheme, respectively. Two power allocation optimization problems are formulated: the first problem minimizes the weighted sum of the average symbol error probability of both PU and SU systems, whereas the second problem maximizes the total achievable sum rate of the PU and SU networks. Lagrangian multiplier method is used to find the optimal solutions for both problems under the constraint of maximum allowable power budget. In addition, the paper investigates how the proposed model improves the PU physical layer security performance against a single passive eavesdropper. The results show that the error performance of the proposed relay selection model outperforms the conventional two-way relaying networks with AF protocol. Moreover, findings illustrate that the total achievable sum rate of the proposed relay elimination model is higher than the total achievable rate of the conventional two-way relaying model. From secrecy point of view, the proposed model is shown to achieve a nonzero secrecy rate that improves the PU system security against eavesdropping attacks. [ABSTRACT FROM AUTHOR]

This paper focuses on a heterogeneous scenario in which cellular and wireless local area technologies coexist and mobile devices are enabled with device-to-device (D2D) communication capabilities. In this context, this paper assumes a network architecture in which a given user equipment (UE) can receive mobile service either by connecting directly to a cellular base station (BS) or by connecting through another UE that acts as an access point (AP) and relays the traffic from a cellular BS. This paper investigates the optimization of the connectivity of different UEs with the target to minimize the total transmission power. An optimization framework is presented, and a distributed strategy based on Q-learning and softmax decision-making is proposed as a means to solve the considered problem with reduced complexity. The proposed strategy is evaluated under different conditions, and it is shown that the strategy achieves a performance very close to the optimum. Moreover, significant transmission power reductions of approximately 40% are obtained with respect to the classical approach, in which all UEs are connected to the cellular infrastructure. For multicell scenarios, in which the optimum solution cannot be easily known a priori, the proposed approach is compared against a centralized genetic algorithm. The proposed approach achieves similar performance in terms of total transmitted power while exhibiting much lower computational requirements. [ABSTRACT FROM PUBLISHER]

In this paper, an initial attempt to exploit the benefits of machine learning in solving relay selection for dualhop networks has been made. From data-driven perspective, we convert relay selection to multiclass-classification problem and propose a decision-tree-based selection scheme. Input features are generated by binary quantization to the equivalent channel state information of each relay, so that the dependence of relay selection on maximization algorithm is removed. To optimize quantization parameter, we derive and maximize two splitting criteria—information gain and Gini index. Experimental results show that decision-tree-based scheme achieves comparable performance to optimal selection in terms of average end-to-end rate while reducing computational complexity and feedback amount. [ABSTRACT FROM AUTHOR]

Considering that wireless unidirectional full-duplex (FD) transmission is one of the FD transmissions over 5G mobile wireless networks, this paper proposes a joint receive and transmit relay beamformer design for systems consisting of a source, a FD relay, an intended destination, and an eavesdropper. The source and destination are single-antenna nodes, whereas the FD relay is a multi-antenna node. Assuming that full channel state information (CSI) is available and the eavesdropper processes two blocks of received data at a time, the joint optimization problem is reformulated as a semidefinite relaxation (SDR) problem (in terms of relay transmit beamformer), which is tight in the sense that the rank-one beamformer can always be recovered from the SDR solution. In order to significantly minimize the computational cost of the SDR implementation, a novel semi-analytical approach based on dual optimization is proposed to obtain the optimum transmit beamformer. When the number of data blocks is arbitrary, we propose a successive convex approximation approach for solving the original non-convex optimization problem and show that a stationary solution can be efficiently obtained. We then propose a distributionally robust beamformer design for the case of an arbitrary number of blocks, assuming that the means and covariance matrices of the eavesdropper's channels are known. Computer simulations show that the proposed semi-analytical solution matches with that obtained from numerical optimization of the SDR problem, the FD scheme significantly outperforms its half-duplex counterpart, and the proposed robust design provides robustness against CSI uncertainty. [ABSTRACT FROM AUTHOR]

This paper studies joint source and relay beamforming for a wireless powered downlink multirelay multiuser network. Considering nonlinear energy harvesting and imperfect channel state information, we aim to minimize the total transmit power at the base station by jointly optimizing the source beamforming and the relay beamforming weights under the energy causality constraints at the relays and the signal-to-noise ratio constraints at the users. The formulated problem is highly nonconvex, and thus, it is difficult to solve. To solve the problem, we first transform it into a worst-case optimization, and then, an iterative algorithm is developed to solve this worst-case optimization. Numerical results show the advantage of the proposed robust scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider unmanned aerial vehicle (UAV) assisted communication systems, where a number of UAVs are utilized as multi-tier relays between a number of users and a base-transceiver station (BTS). We model the wireless propagation channel between the users and the BTS as a Rayleigh product channel, which is a product of a series of independent and identically distributed (i.i.d.) Rayleigh multi-input multi-output channels. We put a special interest in optimizing the number of tiers in such UAV-assisted systems for a given total number of UAVs through maximizing the ergodic capacity. To achieve this goal, in the first part, we derive a lower-bound in a closed form for the ergodic capacity, which is shown to be asymptotically tight. In the second part, we analyze the optimal number of UAV-tiers with the derived bound, and propose a low-complexity solution that significantly reduces the search-size and yields near-optimal performance. Further, asymptotic properties both for the ergodic capacity and the solution for optimal number of tiers are extensively analyzed. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of imperfect channel state information on the performance of the cooperative non-orthogonal multiple access (NOMA) transmission scheme, in which the cell-center user acts as a decode-and-forward relay to assist the information transmission. The cell-center user adopts a simultaneous wireless information and power transfer (SWIPT) technique to harvest energy for information forwarding. Based on two channel error models, a maximization problem is formulated to jointly design the robust beamforming and the power splitting ratio while satisfying the minimum data rate for the far user and the successfully decoding requirement for the information of the far user at the near user. For the worst case design under the deterministic error model, the successive convex approximation and the semidefinite relaxation technique are utilized to approximate the non-convex problem to an iterative convex problem. For the outage-constrained design under the stochastic error model, Bernstein-type inequality-based and large deviation inequality-based approaches are used to safely approximate the probabilistic constraints of the inner-level problem to the deterministic constraints, and a golden section search algorithm is employed to find out the optimal single variable of the outer-level problem. Furthermore, the rank proof is provided to prove that the relaxation is tight. [ABSTRACT FROM AUTHOR]

Motivated by the need to ensure timely delivery of information (e.g., status updates) in the Internet-of-Things (IoT) paradigm, this paper investigates the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak age-of-information for a source–destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. Closed-form solutions for some sub-problems are also provided. One of the sub-problems we solve in this procedure is to jointly optimize the energy and service time allocations for a given trajectory of the UAV. This problem is of interest on its own right because in some cases we may not be able to alter the UAV's trajectory based on the locations of the IoT devices (especially when its primary mission is something else). Our numerical results quantify the gains that can be achieved by additionally optimizing the UAV's trajectory. [ABSTRACT FROM AUTHOR]

Integrating radio frequency energy harvesting into wireless communications to support simultaneous wireless information and power transfer (SWIPT) is a promising technique to extend lifetime of energy-limited wireless cooperative networks. In this paper, multiuser resource optimization for incremental amplify-and-forward orthogonal frequency division multiplexing relaying systems with SWIPT is investigated. To maximize the overall achievable rate, multi-dimensional degrees of freedom (e.g., power allocation, subcarrier assignment, incremental policy, and power splitting ratio) are jointly optimized, subject to the transmission power as well as the quality of service constraints. The optimization problem is formulated as a mixed-integer nonlinear programming and the corresponding solution entails prohibitively high computational complexity. This motivates us to find a suboptimal solution with the aim of realizing a better tradeoff between performance and complexity. Specifically, dual decomposition and subgradient algorithm are applied to achieve this goal and the essence of our proposal is that the variables to be optimized are sequentially and iteratively derived. Finally, complexity analysis and simulation results are shown to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) communications, which provide wireless connectivity among vehicles, roadside drivers, passengers, and pedestrians, are attracting great interest. In this paper, we propose an energy-efficient relay assisted transmission scheme based on V2X communications in the uplink cellular networks for delay insensitive applications. We aim to maximize energy efficiency of the uplink while considering both the circuit power and transmit power consumption, as well as the delay constraint. The optimal resource allocation for direct transmission mode and V2X-enabled transmission mode are derived. For the V2X-enabled transmission mode, we first consider the transmission without the delay constraint and obtain the corresponding optimal power allocation. Based on the obtained result, we further propose the optimal resource allocation policy that satisfies the delay constraint and minimizes the energy consumption. Simulation results show that there exists an energy-delay tradeoff and the proposed V2X-enabled scheme can achieve significant energy saving with an acceptable delay compared with the traditional direct scheme, the decoding-and-forward based transmission scheme, and the opportunistic store-carry and forward based transmission scheme, especially when the cell radius is not small. [ABSTRACT FROM AUTHOR]

This correspondence paper studies joint beamforming optimization and time allocation for a wireless powered two-way hybrid-relay network. As a general relaying architecture accommodating the existing relaying architectures in the literature as two special cases, we consider the two-way communication system where both energy-harvesting (EH) relays and non-EH relays exist. We aim to maximize the minimum rate by jointly optimizing the distributed beamforming vector at the relays, the energy beamforming matrix at the non-EH relays, and the EH time allocation with the energy constraints at the relays. The formulated problem is intractable to solve because the objective function and the energy constraints are highly nonconvex. We solve the optimization problem using the successive convex approximation. Simulations demonstrate that the proposed iterative algorithm considerably outperforms any existing approaches. [ABSTRACT FROM AUTHOR]

Multiantenna full-duplex (FD) relaying, as a promising solution for future wireless communications, has received many attentions. In this paper, focusing on FD decode-and-forward relaying with only partial channel state information available, we are interested in the relay beamforming optimization from an outage probability minimization perspective. Due to the implicit outage probability objective function, it is rather hard to find an optimal solution directly. To overcome this obstacle, we first obtain an explicit form of the outage probability, which involves intricate Marcum Q-functions. Based on the derived objective, we find out the optimal relay receive and transmit beamforming directions, which turns out to be the maximum-ratio combining/maximum-ratio transmission strategy. Then, we rigourously prove that the optimal relay transmit power is the unique solution to a strictly quasi-concave problem and, hence, can be efficiently determined. The performance advantage of the proposed optimal design over conventional schemes is validated via simulations. [ABSTRACT FROM AUTHOR]

In networks with multiple wireless nodes and especially in fast changing environments, the continuous acquisition of channel state information (CSI) may become infeasible. In this correspondence paper, we study the relay selection problem for a cooperative network, where full-duplex relays are equipped with buffers and only statistical CSI is available at the transmitters (CSIT). For this setting, a novel relay selection algorithm is proposed, named $\mathop {\mathrm{HyQoS}}$ , relying only on statistical CSIT and performing power adaptation. $\mathop {\mathrm{HyQoS}}$ solves an optimization problem for selecting the optimal set of relays. We consider two optimization approaches, first, minimization of the total power consumed in the network, and, second, maximization of the throughput per energy unit, while guaranteeing a specific quality of service, in terms of successful transmission probability. The power allocation problem is formulated as a nonlinear optimization one, taking into account inter-relay interference and residual loop interference. Numerical results show that $\mathop {\mathrm{HyQoS}}$ can reduce the power expenditure and increase the throughput, by using only statistical CSIT. [ABSTRACT FROM AUTHOR]

This paper presents an optimization approach for a system consisting of multiple bidirectional links over a two-way amplify-and-forward relay. Improvement to the fairness of the system is desired. All user pairs exchange information over one relay station (RS) with multiple antennas. Due to the joint transmission to all users, the users are subject to mutual interference. A mitigation of the interference can be achieved by max–min fair precoding optimization where the relay is subject to a sum power constraint. The resulting optimization problem is nonconvex. This paper proposes a novel iterative low-complexity approach based on a modified Levenberg–Marquardt (LM) method to find near optimal solutions. The presented method finds solutions close to the standard convex-solver-based relaxation approach. [ABSTRACT FROM PUBLISHER]

In this paper, we study the impacts of cochannel interference (CCI) and channel estimation error (CEE) on the performance of a cellular multiuser two-way relay network. The network comprises of one Na-antenna base station (BS), $M$ single-antenna users, and one L$-antenna relay with N_{r} number of interferers. In particular, by employing antenna selection at the relay, we propose beamforming and antenna selection transmission schemes at the BS with user scheduling in the presence of CEE and CCI. Then, we derive the tight closed-form lower bound expressions for the overall outage probability under Nakagami-m fading. We also deduce the asymptotic outage expressions in the high signal-to-noise ratio regime, from which the impacts of CEE and CCI on the achievable diversity order are highlighted. Moreover, using the asymptotic expressions, three optimization problems to minimize the outage probability are formulated and solved: first, optimal power allocation (OPA) with fixed relay location. Specifically, we develop two OPA approaches, namely, OPA I and OPA II. OPA I allocates the total power appropriately at each terminal, whereas OPA II allocates the fixed half of the total power at the relay and remaining power to base station and users; second, optimal relay location with fixed power allocation; and third, jointly optimal power allocation and relay location. Our results reveal that the OPA I outperforms OPA II, irrespective of different channel/system parameters, interferers, and relay locations. In addition, we demonstrate that the effect of interferers at the relay can be ignored if we allocate sufficient power to the base station and users. It is also shown that the CEE and CCI can severely degrade the outage performance, and even reduce the diversity order to zero. Further, it is highlighted that the impact of CCI can be alleviated by increasing the number of relay antennas. The numerical and simulation results validate our analytical findings, and also demonstrate that the antenna selection scheme offers comparable outage performance as beamforming scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a cooperative network, where relays are capable of simultaneous wireless information and power transfer with a power splitting scheme, and study the optimal design for power splitting ratio at each relay, downlink power allocation, channel assignment, as well as relay association to maximize the weighted sum rate with both amplify-and-forward and decode-and-forward protocols. In essence, such a resource allocation problem is a mashup of continuous-variable optimization and combinatorial optimization. To solve such a challenging problem, we first investigate the optimal solution for power-split ratio in single-user cooperative communication, based on which we propose a dynamic programming solution by exploiting the optimal substructure property of the original problem. To further reduce computational complexity, we propose a polynomial-time heuristic algorithm by leveraging local search strategy, which decomposes the original problem into three subproblems (i.e., downlink power allocation, channel assignment, and relay association) and alternately solves each one given the other two such that the weighted sum rate can be iteratively improved. Simulation results show that the proposed resource allocation scheme outperforms a baseline scheme based on max-sum criterion. Moreover, the proposed local search solution is fairly close in performance to the dynamic programming solution under various parameter settings while the computational complexity is significantly reduced. [ABSTRACT FROM PUBLISHER]

Integrating simultaneous wireless information and power transfer (SWIPT) into cooperative cognitive radio networks (CCRN) can increase both energy and spectral efficiencies. In contrast to most of existing works that consider only relay transmission, this paper studies opportunistic relaying along with dynamic energy harvesting in SWIPT-based CCRN, where a secondary transmitter (ST) can act as relay for primary transmission and a secondary receiver (SR) has the energy harvesting function. We propose a new framework that can unify the direct transmission, relay transmission and energy harvesting. Under the proposed framework, we first assume the transmit power of the ST is fixed and formulate the optimization problem of maximizing the ergodic capacity for SR while meeting the ergodic capacity for the primary receiver and average harvested energy for SR, by joint transmission mode selection and transmit power splitting ratio selection. Then, for the case when the transmit power of the ST can be adjusted, we investigate the joint optimization of transmit power control, transmit power splitting ratio, and transmission mode selection. The two problems are nonconvex and we propose the optimal policies by using the dual Lagrangian method. Simulations validate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the cross-layer optimization of caching and fast video delivery for enhanced video streaming quality of experience in two-hop relaying networks, where a base station supplies video data to multiple users with the help of relays. Different from conventional systems, each half-duplex relay node is equipped with a cache and a buffer to facilitate joint scheduling of video fetching and delivery. This introduces channel diversity gains and facilitates fast video delivery. In particular, we investigate two-stage caching and delivery control schemes for the minimization of the overall video delivery time. An offline caching and delivery optimization problem, which assumes full knowledge of user requests and channel state information (CSI), is formulated but turns out to be functional and nonconvex. However, we unveil a hidden quasi-convexity and convexity in the two layers of the decomposed problem and, hence, solve the offline problem optimally and efficiently. Moreover, online video delivery control exploiting statistical CSI is investigated under a stochastic dynamic programming (DP) framework. To mitigate the high computational complexity of DP, we further propose a low-complexity online video delivery algorithm, which achieves close-to-optimal performance in the high buffer capacity regime. Simulation results show that our offline and online schemes can significantly reduce the overall video delivery time due to the degrees of freedom enabled by caching and buffering. Besides, an interesting tradeoff between caching and buffering gains in exploiting the diversity of the wireless channel is revealed. [ABSTRACT FROM AUTHOR]

In this paper, an amplify-and-forward two-way relay (TWR) precoding is designed with no source node (SN) processing in order to maximize sum rate and concurrently reduce the network latency and signaling overhead. For the arbitrary pre-/postprocessing at the SNs that have $N$ antennas, it is rigorously shown that an $N$ -by-$N$ optimal TWR precoder matrix is symmetric when the noise power of the TWR node is relatively small compared with that of the SNs and/or each of the channel matrices is spatially orthogonal. Using the symmetric structure of the TWR precoding matrix, a constrained nonlinear-multivariable optimization problem is formulated that can be solved with polynomial time complexity. Furthermore, a closed-form optimal TWR precoding matrix is designed for a TWR system with $N=2$. When compared with an optimal joint SN and RN precoding method, it is verified that the proposed precoding method provides a good tradeoff between the system complexity and sum rate performance. [ABSTRACT FROM PUBLISHER]

In this paper, the source and relay transmit covariance matrices are jointly optimized for a fading multiple-antenna relay channel when the transmitters only have partial channel state information (CSI) in the form of covariance feedback. For both full-duplex and half-duplex transmissions, we evaluate lower and upper bounds on the ergodic channel capacity. These bounds require a joint optimization over the source and relay transmit covariance matrices. The methods utilized in the previous literature cannot handle this joint optimization over the transmit covariance matrices for the system model considered in this paper. Therefore, we utilize matrix differential calculus and propose iterative algorithms that find the transmit covariance matrices to solve the joint optimization problem. In this method, there is no need to specify first the eigenvectors of the transmit covariance matrices. The algorithm updates both the eigenvectors and the eigenvalues at each iteration. Through simulations, we observe that lower and upper bounds are close to each other. However, the distance between the lower and upper bounds depends on the channel conditions. If the mutual information on the source-to-relay channel and the broadcast channel get closer to each other, the bounds on capacity also get closer. [ABSTRACT FROM PUBLISHER]

With macro and micro base stations (BSs) of different transmission and processing capabilities jointly deployed, heterogeneous networks effectively extend the coverage and capacity of wireless networks and have been adopted as one of the key technologies in Fourth-Generation-Long-Term Evolution (4G-LTE). To fully exploit the benefits of heterogeneous networks, proper mobile association, interference management, and radio resource management schemes need to be developed. In this paper, we investigate radio resource allocation for heterogeneous networks with cooperative relays, where the relay nodes (RNs) with in-band backhaul act as micro BSs and are able to serve user equipment (UE) either independently or cooperatively with the BSs. In such a network, radio resource-allocation schemes need to decide for each resource block: 1) whether a UE is served by a BS or an RN, and 2) whether a UE should be cooperatively served or not. In making these decisions, radio resource consumption on RN's in-band backhaul links and fairness among the UEs should be taken into consideration, which further complicates the resource-allocation problem. In this paper, we propose a radio resource-allocation framework and derive a resource-allocation strategy that is asymptotically optimal on the proportional fairness metric. The derived resource-allocation scheme gives insights on the optimal radio resource allocation for the heterogeneous networks with cooperative RNs using in-band backhauls. [ABSTRACT FROM PUBLISHER]

The ever-increasing demand for mobile Internet and high-data-rate applications poses unique challenging requirements for 5G mobile networks, including spectrum limitations and massive connectivity. Cognitive radio and carrier aggregation (CA) have recently been proposed as promising technologies to overcome these challenges. In this paper, we investigate joint relay selection and optimal power allocation in an underlay cooperative cognitive radio with CA, taking into account the availability of multiple carrier components in two frequency bands, subject to outage probability requirements for primary users (PUs). The secondary user network employs relay selection, where the relay that maximizes the end-to-end sum rate is selected, assuming both decode-and-forward and amplify-and-forward relaying. The resulting optimization problems are optimally solved using convex optimization tools, i.e., dual decomposition and an efficient iterative method, allowing their application in practical implementations. Simulation results illustrate that the proposed configuration exploits the available degrees of freedom efficiently to maximize the SU rate, while meeting the PU average outage probability constraints. [ABSTRACT FROM PUBLISHER]

We consider cooperative communications with energy harvesting (EH) relays and develop a distributed power control mechanism for the relaying terminals. Unlike prior work, which mainly deals with single-relay systems with saturated traffic flow, we address the case of bursty data arrival at the source cooperatively forwarded by multiple half-duplex EH relays. We aim at optimizing the long-run average delay of the source packets under the energy neutrality constraint on the power consumption of each relay. While EH relay systems have been predominantly optimized using either offline or online methodologies, we take on a more realistic learning-theoretic approach. Hence, our scheme can be deployed for real-time operation without assuming acausal information on channel realizations, data/energy arrivals as required by offline optimization, or relying on precise statistics of the system processes, as is the case with online optimization. We formulate the problem as a partially observable identical payoff stochastic game (PO-IPSG) with factored controllers in which the power control policy of each relay is adaptive to its channel and energy states as well as to the state of the source buffer. We equip each relay with a reinforcement learning procedure and prove that the parallel execution of this procedure is convergent to (at least) a locally optimal solution of the formulated PO-IPSG. The proposed algorithm operates without explicit message exchanges between the relays, while inducing only little source-relay signaling overhead. By simulation, we contrast the delay performance of the proposed method against existing heuristics for throughput maximization. It is shown that compared with these heuristics, the systematic approach adopted in this paper has a smaller suboptimality gap once evaluated against a centralized optimal policy armed with perfect statistics. [ABSTRACT FROM AUTHOR]

In this paper, we consider the power-allocation problem in orthogonal frequency-division multiplexing (OFDM)-based bidirectional cognitive radio (CR) relay networks. A joint optimization under the amplify-and-forward (AF) relaying protocol is formulated to maximize the throughput of secondary users (SUs) via power loading over different subcarriers at the three (two transceivers and a relay) nodes. For compatibility with practical systems, separate power constraints are considered at each transmitting node. Furthermore, to provide high-level protection to primary users (PUs) in underlay CR transmission, per-subcarrier-based interference constraints are assumed. For tractability of the solution, we adopt a decomposition framework where the joint power optimization (JPO) is split into two subproblems. Later, each subproblem is solved by exploiting the convex optimization techniques. Moreover, a low-complexity suboptimal solution of the joint power allocation at all nodes is also proposed. Simulation results are presented to validate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

This paper investigates the simultaneous wireless information and power transfer (SWIPT) in cooperative relay networks, where a relay harvests energy from the radio frequency (RF) signals transmitted by a source and then uses the harvested energy to assist the information transmission from the source to its destination. Both source and relay transmissions use rateless codes (RCs), which allow the destination to employ any of the two information receiving strategies, i.e., the mutual information accumulation (IA) and the energy accumulation (EA). The SWIPT-enabled relay employs three different SWIPT receiver architectures, the ideal receiver, and two practical receivers (i.e., the power splitting (PS) receiver and the time switch (TS) receiver). Accordingly, three relaying protocols, namely, the ideal protocol, PS protocol, and TS protocol, are presented. To explore the system performance limits with these three protocols, optimization problems are formulated to maximize their achievable information rates. For the ideal protocol, explicit expressions of the optimal solutions are derived. For the PS protocol, a linear-search algorithm is designed to solve the nonconvex problems. For the TS protocol, two solving methods are presented. Numerical experiments are carried out to validate our analysis and algorithms, which show that, with the same SWIPT receiver, the IA-based system outperforms the EA-based system, whereas with the same information receiving strategy, the PS protocol outperforms the TS protocol. Moreover, compared with nonrateless-coded systems, the proposed protocols exhibit considerable performance gains. Moreover, the effects of the relay position on system performance are also discussed, which provides insights on SWIPT-enabled relay systems. [ABSTRACT FROM AUTHOR]

A full-duplex (FD) two-way relay channel (TWRC) with multiple antennas is considered. For this three-node network, the beamforming design needs to suppress self-interference (SI). While a traditional way is to apply zero forcing (ZF) for SI mitigation, it may harm the desired signals. In this paper, a design that reserves a fraction of SI is proposed by solving a quality-of-service (QoS) constrained beamforming design problem. Since the problem is challenging due to the loop SI, a convergence-guaranteed alternating optimization (AO) algorithm is proposed to jointly design the relay–user beamformers. Numerical results show that the proposed scheme outperforms ZF method and achieves a transmit power value close to the ideal case. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a limited rate feedback scheme for decode-and-forward (DF) relay-assisted networks. It is assumed that the cellular user is far from the base station such that the direct communication is impossible and communication is performed with the help of a DF relay. To study the limited feedback channel, two cases are proposed: Perfect channel distribution information (CDI)-quantized channel state information (CSI) and quantized CDI-quantized CSI. While in the former, the space of channel gains is quantized into a finite number of regions, in the latter case, the space of CDI parameters is also quantized. We formulate the proposed resource allocation into an optimization problem whose objective is to maximize the average achievable rate under an average transmission power constraint. To solve the proposed optimization problems, we adopt the block coordinated descent algorithm, which is based on the space of variables partitioning into finite sets, and particle swarm optimization (PSO), which is an evolutionary computation technique. To solve the optimization problem for quantized CDI-quantized CSI case, we consider an approach based on the Lloyd algorithm. We also model and study the error in the feedback channel. Finally, the impact of different system parameters on the performance of the proposed scheme is investigated through simulations. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the relay selection and power allocation (RSPA) problem in amplify-and-forward (AF) relaying networks. The objective is to minimize the outage probability at the receiver, assuming that only the mean channel gain information is available. By taking advantage of its special structure, we propose to decompose the RSPA problem into two tractable subproblems, which can be implemented in a semidistributed manner. For the relay selection (RS) subproblem, a relay-ordering-based scheme is designed to incrementally select a subset of relays from the candidate nodes according to a specific metric in a centralized manner, which is dominated by the mean channel gain of each individual nodes. For the power allocation (PA) subproblem, a source-driven iterative algorithm is proposed for each relay to decide its optimal transmit power locally by exploiting the convexity of this subproblem. Simulation results are provided to evaluate the outage and signal-to-noise-ratio (SNR) performance of the proposed scheme under different channel conditions. It is shown that the proposed scheme can dynamically select relays and adjust power allocation according to the SNR and channel conditions. It outperforms the other two benchmark schemes, and the performance is close to the optimal scheme with exhaustive search, but the computational complexity is significantly reduced. [ABSTRACT FROM AUTHOR]

In this paper, we consider energy usage in a three-node, two-way relay system with stochastic data arrivals and Gaussian channels. Specifically, we allow for a set of transmission modes, assuming a multiaccess uplink transmission with the relay as an optimal multiuser receiver, and find the optimal allocation of time and energy resources to the modes. In the downlink, we introduce three transmission strategies, namely, orthogonal transmission of a digital network-coded message and the remainder of the longer source message over time, superposition coding of the digital network-coded message and the remainder of the longer message, and superposition of the two source messages. We then minimize total energy usage subject to queue stability constraints at all nodes for a given pair of average packet arrival rates. The total energy usage of these three strategies in the downlink is compared, and it is found that it is best to use superposition of the network-coded message and the remaining bits in the downlink. To study stability, we present an analysis of the queues at each node under a random scheduling protocol that closely approximates the theoretical design through a Markov chain model. [ABSTRACT FROM PUBLISHER]