Relay selection based on coalitional game for secure wireless networks

This letter proposes a deep reinforcement learning (DRL) based relay selection scheme for cooperative networks with the intelligent reflecting surface (IRS). We consider a practical phase-dependent amplitude model in which the IRS reflection amplitudes vary with the discrete phase-shifts. Furthermore, we apply the relay selection to reduce the signal loss over distance in IRS-assisted networks. To solve the complicated problem of joint relay selection and IRS reflection coefficient optimization, we introduce DRL to learn from the environment to obtain the solution and reduce the computational complexity. Simulation results show that the throughput is significantly improved with the proposed DRL-based algorithm compared to random relay selection and random reflection coefficients methods.

Energy harvesting (EH) is an attractive solution to prolong the lifetime of wireless devices. With EH capability, an intermediate node, acting as the relay, can extract energy from the signal of a source node and use the harvested energy to perform information relaying, enabling a self-sustainable cooperative network. To efficiently use the harvested energy, it is important to select proper relays to receive and forward the source signal. In this paper, we investigate the relay selection (RS) problem for EH relays with short-term energy storage. A relay selection scheme, called selective max-max relay selection (S-MMRS), is proposed aiming to exploit diversity gain that is not achieved in the previous work. To evaluate the performance of S-MMRS, we theoretically analyze its outage probability. Simulation results are presented to verify the analysis accuracy and demonstrate that S-MMRS is a diversity achievable scheme.

Relay selection is crucial in improving the performance of wireless cooperative networks. Most of previous works for relay selection use the current observed channel conditions to make the relay selection decision for the subsequent frame. However, this assumption is often not realistic given the time-varying nature of some mobile environments. In this paper, we consider finite state Markov channels in the relay selection problem. Moreover, we also incorporate adaptive modulation and coding, as well as residual relay energy in the relay selection process. The objectives of the proposed scheme are not only to increase spectral efficiency, mitigate error propagation, but also to maximize the network lifetime. Simulation results are presented to show the effectiveness of the proposed scheme.

In cooperative relay networks with multiple users and multiple potential relays, which relay nodes are selected has great impact on the system performance. It is an optimization problem for selecting suitable relay nodes. The exhaustive search can solve this problem but the complexity will increase factorially with the network size, i.e., the number of users and the number of relays in the network. In this paper, we formulate both single-objective and multi-objective relay selection problems. For single-objective relay selection problem, only system throughput or fairness is considered. A novel quantum particle swarm optimization (QPSO) based relay selection scheme is proposed. For multi-objective relay selection problem, two contradictive objectives, i.e., system throughput and fairness are considered simultaneously. A novel non-dominated sorting quantum particle swarm optimization (NSQPSO) based relay selection scheme is proposed. Simulation results show that the proposed relay selection schemes have the ability to search global optimal solution but have less computational complexity compared with exhaustive search.

We consider the problem of relay selection in a scenario with N relay nodes. A half-duplex relay node enables bidrectional communication between two nodes with a spectral efficient two phase protocol. In the first phase, both nodes transmit a message to a relay node, which decodes the messages and broadcasts a composition in the succeeding phase. The relay selection criteria decides for any bidirectional rate pair on the boundary of the achievable rate region for the relay node which achieves the largest weighted rate sum. With time-sharing between the usage of relay nodes the achievable rate region can be further increased. In a iid Rayleigh fading scenario relay selection realizes multi-user space diversity so that the sum-rate of any rate pair on the boundary of the ergodic rate region scales like O(log(log(N))).

Cooperative communication in wireless relay systems can improve the physical layer security, and the relay selection has great impacts on the security performances. In this paper the relay selection in Amplify-and-Forward (AF) cooperative networks is investigated. The system model constitutes of a source, an eavesdropper, a destination, and a number of intermediate nodes with secrecy constraints. The proposed optimal selection (OS) algorithm is based on the instantaneous channel state information (CSI) of two hops, jointly protects the target destination against eavesdropping and interference. To simplify the complexity of OS, the suboptimal selection (SS) algorithm is also presented, which is based on the instantaneous of the first hop and the statistical CSI of the second hop respectively. The secrecy capacity and secrecy outage probability of these two relay selection algorithms are analyzed. Simulation results of OS and SS validate our theoretical analysis and show enhancement in the secrecy rate over the direct transmission.

A novel cross-layer joint relay selection and power allocation (CL-JRSPA) scheme over physical layer and data-link layer is proposed for cooperative relaying system in this paper. Our goal is finding the optimal relay selection and power allocation scheme to maximize system achievable rate when satisfying total transmit power constraint in physical layer and statistical delay quality-of-service (QoS) demand in data-link layer. Using the concept of effective capacity (EC), our goal can be formulated into an optimal joint relay selection and power allocation (JRSPA) problem to maximize the EC when satisfying total transmit power limitation. We first solving optimal power allocation (PA) problem with Lagrange multiplier approach, and then solving optimal relay selection (RS) problem. Simulation results demonstrate that CL-JRSPA scheme gets larger EC than other schemes when satisfying delay QoS demand. In addition, the proposed CL-JRSPA scheme achieves the maximal EC when relay located approximately halfway between source and destination, and EC becomes smaller when the QoS exponent becomes larger.

In this paper, we derive the Symbol Error Probability (SEP) of Round Robin (RRS), Centralized (CRS) and Distributed Relay Selection (DRS) techniques for Free Space Optical communications. RRS consists to randomly select a node as relay with equal probability. It does not consider the SNR value for relay selection. In CRS, the SNRs of different relays are sent to a Central Node (CN) for detecting the relay with highest SNR. The CN selects the best relay with largest SNR. We propose a new DRS where each relay is allowed to transmit only when its SNR is higher than a given threshold T. The threshold T is optimized with the Gradient algorithm to guarantee the lowest SEP. The suggested DRS with optimal threshold allows close performance to CRS and better performance than RRS. DRS is less complex than CRS since no signalization is needed.

This work considers the relay selection and resource allocation problem (i.e., link scheduling, and rate allocation) for multi-source, multi-relay dual-hop wireless networks. The relays employ buffers to store the received data from the sources for future transmissions. End-to-end (E2E) delay of each traffic flow originated from a source or a relay is constrained in terms of maximum allowable delay-outage probability. To solve this problem, we first study the resource allocation problem to maximize the constant supportable arrival rate of a non-prioritized source under minimum rate requirements of the prioritized sources and relays for a given relay selection solution. Then, the optimal relay selection can be determined to support the largest rate of the non-prioritized source among all possible relay selection solutions. We derive the resource allocation solutions using asymptotic delay analysis and convex optimization techniques. We also develop an online allocation algorithm which does not require the knowledge of the fading statistics by using stochastic approximation theory. Numerical results are presented to demonstrate the usefulness of the proposed resource allocation design for relay selection under different delay and rate constraint regimes.

With the advance of the photovoltaic panel technology, the infrastructure equipment of cellular networks with solar energy sources has attracted extensive interests. It is expected that base stations (BSs) and relay stations (RSs) with renewable energy will play important roles in reducing the power consumption from electrical grids in future green cellular networks. Inspired by this technology trend, this paper considers a heterogeneous green cellular network, in which a part of RSs is powered by solar energy sources to represent the incremental deployment for the infrastructure with renewable energy in the near future. We formulate a new relay selection and power allocation problem with Mixed Integer Linear Programming to select solar-powered RSs and grid-powered RSs, such that the total grid power consumption is effectively minimized in decode-and-forward cooperative communication networks. We design an algorithm to derive the optimal solution, and also devise an efficient distributed algorithm to reduce the computational cost. The simulation results demonstrate that the algorithms can effectively reduce the total grid power consumption with different channel conditions, different deployments of RSs in a network, and different available power levels in solar-powered RSs. In addition, the problem model and the proposed algorithms can be extended to support hybrid energy sources.

In this paper, relay selection, subcarrier pairing and power allocation are investigated jointly to maximize the channel capacity for OFDM-based cooperative networks under a total power constraint. The problem can be formulated as a mixed binary integer programming problem, which is NP-hard and very difficult to manipulate. In order to achieve an acceptable performance with a proper complexity, we first solve the relay selection and subcarrier pairing problem under equal power allocation. Then, power allocation is performed according to the equivalent channel power gains of the subcarrier pairs using water-filling. Simulations are carried out to validate our analysis and show that the proposed scheme can achieve an acceptable capacity with a proper complexity.

In this paper, we consider the problem of relay selection in a cognitive full-duplex two-way multiple-input multiple-output relaying network. The cognitive nodes operate on the same frequency as the licensed primary user (PU). The end-to-end communication between the secondary users (SU) via a secondary relay (SR) takes two time slots. The SR operates on amplify-and-forward (AF) protocol. In the first time slot, the SR relay with the highest receive signal-to-noise ratio is selected as the optimal relay. At the same time, the precoders at the SU are designed to limit the interference to the PU below a certain threshold. All the nodes in the network are assumed to have imperfect knowledge of channel state information of their loopback self-interference channels and hence experience residual self-interference (RSI). After selecting the optimal SR, in the next time slot, we design the SR precoder and SU receive filters, to mitigate the RSI. We do so by minimizing the sum of mean square error of the end-to-end communication with a transmit power constraint at SR. To curb the effect of cumulative RSI caused by the AF operation of the SR, we update the SR precoder and SU receive filters at each time slot. The efficacy of our proposed designs is illustrated in the simulation results.

Virtual multiple-input multiple-output (VMIMO) enables the implementation of conventional MIMO on mobile devices equipped with insufficient numbers of antennas via cooperation. This paper considers a spectral efficient broadcasting network in which selected mobile devices form a VMIMO system to relay the broadcasted data to help other devices decode the source data more reliably. In particular, the relay selection problem, a fundamental issue in the construction of VMIMO, is examined. We first review existing selection schemes for users operating in the amplify-and-forward (AF) mode. We then propose a distributed selection scheme based on post-processing SNR. In the proposed scheme, each user individually finds the most favorable candidates for VMIMO construction and then all users obtain a joint decision through a voting process. Simulation results show that the proposed distributed scheme outperforms existing distributed selection schemes and achieves a near-optimal performance with lower complexity compared to the centralized scheme.

This letter investigates the relay selection problem for the air-to-ground vehicular ad hoc networks (A2G VANETs). Specifically, we use unmanned aerial vehicle (UAV) to enhance VANETs communications and network performance. For the considered network, we calculate the state transition probability (STP) of communication interruption and the transmission consumption (TC) including energy consumption and delay consumption. Afterward, we formulate the relay selection problem as a multi-objective optimization problem by jointly considering the STP and the TC. For tackling this hard problem, we simplify the primal problem by setting the STP threshold and devise a relay selection algorithm with low complexity through Q-learning. Finally, the simulation results exhibit the performance improvement of our algorithm against other schemes in terms of delivery ratio, delivery latency, and signaling overhead.

In order to improve the delay quality-of-service aware throughput performance of the optical wireless communications, a joint power allocation and relay selection scheme is proposed for a relay assisted multichannel coherent optical wireless communication system. The proposed joint power allocation and relay selection scheme provides the delay aware quality-of-service guarantee by maximizing the end-to-end effective capacity under the average transmit power constraints at both source as well as relay nodes. The joint power allocation and relay selection problem is formulated as a non-convex and combinatorial optimization problem, and the solution of this optimization problem is obtained using a time-sharing relaxation technique. Subsequently, a fast convergent algorithm is proposed for the joint power allocation and relay selection by considering the delay aware quality-of-service requirements of the services transmitted over the optical wireless communication channels. Numerical results demonstrate that the proposed joint power allocation and relay selection scheme provides the strict statistical delay aware quality-of-service with an improved throughput without increasing the transmit power requirements.