Relay Power Allocation Research Articles

Joint User Scheduling, Trajectory, Beamforming and Power Allocation for Millimeter-Wave Full-Duplex UAV Relay

Joint User Scheduling, Trajectory, Beamforming and Power Allocation for Millimeter-Wave Full-Duplex UAV Relay

Network sum-rate maximization for network-coded clustered uplink NOMA networks with SWIPT-enabled relays

This paper considers network sum-rate maximization for network-coded clustered uplink non-orthogonal multiple-access (NOMA) networks with simultaneous wireless information and power transfer (SWIPT)-enabled relays. Specifically, the aim is to perform joint power allocation, power-splitting, and relay selection (J-PA-PS-RS), subject to quality-of-service (QoS) requirements. The formulated problem is excessively computationally-intensive, as it is a non-convex optimization problem. To efficiently solve it, it is split into two sub-problems: (1) joint power allocation and power-splitting per relay, and (2) relay selection. Particularly, a solution procedure is proposed, where the joint user and relay power allocation, and power-splitting per relay sub-problem is solved via a low-complexity iterative three-layer algorithm, and then followed by optimal relay selection. Simulation results have revealed that the proposed solution procedure can efficiently be used to maximize the network sum-rate, achieve near-optimal solutions, and outperform other benchmark schemes, while satisfying QoS constraints.

Joint Power Allocation for Transmitter and Relay in a Full-Duplex Relay Covert Communication System

Joint Power Allocation for Transmitter and Relay in a Full-Duplex Relay Covert Communication System

Energy Trading and Power Allocation Strategies for Relay-Assisted Smart Grid Communications: A Three-Stage Game Approach

In smart grid, serious packet loss often occurs in the process of information interaction between the Utilities and customers, which results in supply-demand deviation and further increases the cost of the Utilities. In order to improve the information transmission performance of communication networks, the Utilities purchase relay service from telecom operator to help data aggregator units (DAU) transfer information to gateway (GW), so as to improve communication quality and reduce the cost of the Utilities. Secondly, in order to solve the problem of telecom operators' energy reduction and reduce the cost of purchasing energy, we utilize energy supply point (ESP) to collect the surplus energy of retail customers for energy supply, and telecom operator pays a certain amount of remuneration to ESP in exchange for ESP to continuously supply energy to telecom operator. Then, we establish a three-stage game method and system model between the Utilities, telecom operator and ESP, and propose the relay power allocation and energy transaction pricing strategy. Due to the real-time change of energy demand, we consider two situations of energy oversupply and conservative supply, and use the backward induction method and iterative algorithm to obtain the equilibrium solution of the Stackelberg game, including the unit energy price of ESP, total power of telecom operator, the proportion of transmission power allocated to the relay service, and payment scheme of the Utilities. Simulation results show that the proposed algorithm can quickly and accurately converge to the optimal solution of the problem, and the method can improve the stability of demand-side regulation, reduce the cost of the Utilities and increase the profit of telecom operator.

A New Distributed Robust Power Control for Two-Layer Cooperative Communication Networks in Smart Grids with Reduced Utility Costs

The packet loss during transmission of load control commands can lead to regulation errors in the smart grid and increase the cost of utility agencies due to the purchase of additional automatic generation control (AGC) services. In this paper, a two-layer cooperative communication network between the utility company and relays is presented. The utility company rents the relay to assist with the downlink transmission to improve the reliability of communication and reduce the data transmission cost due to packet loss. Furthermore, the uncertainty of channel gain is considered, and a two-tier game model is established. A distributed robust power control algorithm based on the continuous convex approximation method is proposed to obtain the optimal relay power allocation and price. Through the simulation analysis of the proposed scheme and the two comparison schemes, the cost of the utility company was reduced by 6% and 21%, and the standard deviation of income value between the relays was reduced by 40% and 48%, respectively.

Joint Deployment and Power Optimization for UAV Relay in Multiuser Networks

Within the UAV network, the UAV first receives signals from multiple remote mobile devices (MDs) and then amplifies and forwards the transmitted signals to the base station (BS) with different amplification coefficients to form a UAV relay multiuser network. In this paper, we propose a new method to solve the problem of maximizing the throughput of the relay network. The proposed problem is decoupled into two subproblems, UAV deployment design and amplification coefficient optimization, to be solved iteratively, respectively. We solve the UAV deployment problem by adjusting its trajectory with a gradient descent-based method and solve the amplification coefficient subproblem with a convex optimization-based method iteratively. Simulation shows that the proposed UAV deployment and amplification coefficient of the UAV optimization design algorithm significantly improves the sum-rate compared with existing fixed relay and equal power allocation schemes. Finally, we discuss future potential performance enhancing methods including multiple UAV cooperation, massive multi-input multioutput (MIMO) communications, and nonorthogonal multiple access (NOMA) communications.

An Energy-Efficiency Multi-Relay Selection and Power Allocation Based on Deep Neural Network for Amplify-and-Forward Cooperative Transmission

In this letter, an energy-efficiency (EE) resource allocation strategy is investigated for the amplify-and-forward (AF) protocol used to forward data to the destination. First, we formulate an EE optimization problem of multi-relay AF system, which correlates the power of source with the power of each relay. Then, a deep neural network (DNN)-based model is constructed to implement the joint power allocation of source and multiple relays. In particular, we adopt the modified rectified linear unit (ReLU) as the activation function in the output layer of the DNN model to hinder relays’ negative output power weightings, thereby to simultaneously realize the selection of relays and their power allocation. The simulation results show that our scheme enables better performance in term of the EE of system compared with the conventional strategies and convolutional neural network (CNN).

Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems

In this paper, to maximize the energy efficiency (EE) in the two-hop multi-relay cooperative decoding and forwarding (DF) system for simultaneous wireless information and power transmission (SWIPT), an optimal power allocation algorithm is proposed, in which the relay energy harvesting (EH) adopts a nonlinear model. Under the constraints, including energy causality, the minimum transmission quality of information and the total transmission power at the relays, an optimization problem is constructed to jointly optimize the transmit power and power-splitting (PS) ratios of multiple relays. Although this problem is a nonlinear fractional programming problem, an iterative algorithm is developed to obtain the optimal power allocation. In particular, the joint power allocation at multiple relays is first decoupled into a single relay power allocation, and then single-relay power allocation is performed by the Dinkelbach iteration algorithm, which can be proven that it is a convex programming problem. Its closed form solutions for different polylines of EH models are obtained by using mathematical methods, such as monotonicity, Lagrange multipliers, the KKT condition and the Cardan formula. The simulation results show the superiority of the power allocation algorithm proposed in this paper in terms of EE.

Optimal power allocation for the multiple-sensor single-DF relay based IoT networks under transceiver hardware impairments

Accurate system modeling is a crucial requirement while realizing efficient and reliable communication over Internet of Things (IoT) infrastructure. The assumption of ideal hardware causes huge deviation in the performance of systems as estimated from such theoretical models and their practical implementations. In this paper, we study this fact over N pairs of source and destination IoT devices/nodes having non-interfering channels with a half-duplex decode-and-forward relay node. The relay assists the communication between N source–destination pairs. Specifically, we first derive the outage probability expression for the considered system with transceiver hardware impairments under independent but not identically distributed Rayleigh fading channels. Then, taking the transceiver hardware impairments and power and outage constraints into account, we formulate and investigate an optimal power allocation strategy in order to minimize the source-sum-power consumption using Karush–Kuhn–Tucker (KKT) conditions. With the aid of numerical investigations, the optimality of the proposed strategy is illustrated. It is observed that the power consumption depends on the number of sources and increases with the increased number of sources. In addition, the transceiver hardware impairments have detrimental impacts on the source-sum-power consumption of the considered system. Our results also reveal that the proposed scheme outperforms the uniform power allocation scheme for various involved parameters.

Joint relay selection and power allocation for NOMA‐based multicast cognitive radio networks

In this study, the problem of joint relay selection and power allocation (J-RS-PA) for NOMA-based multicast cognitive radio networks is considered. In particular, the aim is to simultaneously maximise the end-to-end SINR/SNR of the primary and secondary transmitter–receiver (TR) pairs, subject to quality-of-service (QoS) constraints. Communication between the primary and secondary TR pairs is performed over two-phases, namely, the broadcasting phase, and the cooperation phase. In the broadcasting phase, the primary and secondary transmitters broadcast their data symbols; while in the cooperation phase, the selected relay forwards the decoded symbols to their intended receivers. However, the formulated J-RS-PA problem happens to be non-convex, resulting in computationally-prohibitive complexity. Consequently, an optimal low-complexity two-stage relay selection and power allocation (TS-RS-PA) algorithm is devised, which is based on the solution of linear programming problem reformulations. Simulation results are presented to validate the proposed TS-RS-PA algorithm, which is shown to yield the optimal SINR/SNR values in comparison to the J-RS-PA scheme, but with lower computational complexity, while satisfying QoS constraints.

Energy Efficiency Maximization of Relay Aerial Robotic Networks

This paper proposes a new method of maximising the energy efficiency of Aerial Robotic (AR) networks in order to prolong AR battery life. An AR network is composed of an unmanned aircraft (drone) fitted with a wireless relay network. The AR network provides a connection server between two spread ground nodes. It is able to hover at any point of its flight path, which enables the AR network to function as a Mobile Base Station (MBS). An MBS can increase the range and flexibility of a communication network, but this ability increases the AR’s energy consumption, which in turn increases drone battery drain. Therefore, it is essential to select the optimal MBS location so that the drone battery drain is minimised. In this paper we propose a method for evaluating the MBS’s energy consumption at any point on the drone’s flight path and thus select the hovering location of minimum energy consumption as the optimal MBS location. The proposed method optimises the source and relay power allocations on the drone’s flight path. The power allocations are then combined at each MBS location to maximize the energy efficiency metric. Since the power allocations can also influence positively the AR throughput, this enables the AR throughput to be enhanced as well as the energy efficiency. The proposed analytical results are verified by using the MATLAB source code. It is shown that the proposed method can considerably improve both of AR network energy efficiency and throughput compared with conventional designs.

Relay Selection & Power Allocation for Maximizing Sum-Throughput of a Buffered Relay Network

Considering two-hop cooperative communication with buffered half-duplex relays, this work jointly optimizes relay selection and transmit power at the relay to maximize the sum-throughput of the network subject to a minimum throughput requirement of each hop and transmit power constraint. The optimization problem is solved and two new buffer aware selection schemes, namely joint relay power allocation and selection scheme (JPASS) and ratio selection scheme (RSS) are proposed. Numerical results show that the average sum-throughput of both the proposed schemes is up to 40% higher compared to the existing schemes in the literature.

NOMA-Aided Multi-Way Massive MIMO Relaying

For a multi-way relay network (MWRN) with K users, K time slots are needed for full data exchange. Thus, the overall spectral efficiency, due to the 1/K pre-log factor, declines as number of users grows. It has recently been improved to roughly K/2 time slots, but even this improvement does not arrest the decline. Herein, we reduce this task to just two time slots regardless of K. To do this, we exploit the performance gains of non-orthogonal multiple-access (NOMA) and a massive multiple-input multiple-output (MIMO) relay. First, the users transmit their signals to the relay, which uses maximal ratio combining reception. Next, the relay transmits a superposition-coded signal for all users by using maximal ratio transmission. Each user then performs successive interference cancellation (SIC) decoding of data symbols of the other K - 1 user nodes. We use the so-called worst-case Gaussian approximation to derive the overall sum rate and demonstrate significant spectral-efficiency gains and energy-efficiency gains over the existing MWRN counterparts. We also design the relay power allocation matrix to maximize the minimum among the user rates, thus maximizing the user fairness. Furthermore, the effects of imperfect SIC and imperfect channel state information (CSI) on the sum rate are analyzed.

Relay Selection Optimization for SWIPT-Enabled Cooperative Networks

Simultaneous wireless information and energy transfer based on radio frequency (RF) energy harvesting can transmit power and information at the same time and can use the same wireless signal to decode the information and to charge the battery. In order to improve spectrum efficiency of energy-constrained networks, we consider combining cooperative relay with simultaneous information and energy transfer in this paper. The optimal relay power allocation and relay selection algorithms based on statistical channel state information (CSI) and perfect CSI are proposed, respectively. Specifically, the system first selects the relay nodes that can correctly decode the source information and, then among them, selects the node with the best second hop channel condition. We derive the exact analytical outage probability of the proposed algorithms by using order statistical tool. The analytical results are compared and verified by Monte Carlo simulations. It is observed that the outage probabilities of our proposed algorithms based on statistical CSI and perfect CSI are all lower than that of the corresponding traditional max–min algorithms.

Outage probability of fixed‐gain amplify‐and‐forward two‐way relays with multiple co‐channel interferers

In this study, the authors consider a two-way relaying (TWR) system with source terminals T a and T b and a relay terminal T r . They analyse the performance of a fixed-gain amplify-and-forward-based TWR system over Rayleigh fading channels with co-channel interference at all the terminals. Expressions are derived for the outage probability of the system at a medium-to-high signal-to-interference-plus-noise ratio (SINR) range. They also obtain the expression for the value of outage floor and use this to optimise relay location and power allocation. Simulation results demonstrate accuracy of the derived expressions in different SINR ranges.

Outage Analysis of Decode-and-Forward Two-Way Relay Selection With Different Coding and Decoding Schemes

In this paper, we consider a two-way relay with multirelays. We first review the joint decoding (JD), decoding by parts (DP), bit-level exclusive-or (XOR), and superposition coding (SC) schemes for the two-way decode-and-forward relay (TWDR), and then propose a new TWDR selection scheme for the two mentioned coding methods. In particular, we focus on the outage performance analysis of the relay selection strategy using the aforementioned coding and decoding methods. Based on an approximate analysis of the outage performance in the high signal-to-noise ratio, we present the diversity-multiplexing tradeoff for the considered decoding and coding schemes. Moreover, we present an optimal power allocation for relays with the SC mechanism. Our simulation results show that our proposed relay selection strategy is outage optimal for all the coding and decoding methods mentioned in this paper. Our results also show that the outage performance under the JD-Xor and JD-SC schemes is more competitive for the case in which the channel statistical characteristics satisfy ${\bar{\gamma }_a}\sigma _{ka}^2 \approx {\bar{\gamma }_b}\sigma _{kb}^2$ ( $\bar{\gamma }_i = P_i/N_0$ , $i \in \left\lbrace {a,b} \right\rbrace$ , where $a$ and $b$ denote two sources exchanging information with each other, $P_i$ is the average transmitting power from the $i$ source, $N_0$ is the variance of additive white Gaussian noise obtained by the relay $k$ , and $\sigma _{ki}^2$ is the variance of the Rayleigh fading channel between $k$ and $i$ ). When ${\bar{\gamma }_a}\sigma _{ka}^2 \gg {\bar{\gamma }_b}\sigma _{kb}^2$ or ${\bar{\gamma }_a}\sigma _{ka}^2 \ll {\bar{\gamma }_b}\sigma _{kb}^2$ , the outage performance using DP-Xor or DP-SC mechanisms is much better.

Joint Beamforming and Power Allocation for UAV-Enabled Full-Duplex Relay

This paper investigates an unmanned aerial vehicle (UAV) enabled full-duplex relaying system. By assuming that the UAV follows a circular trajectory and applies decode-and-forward relaying strategy, we study the joint design of beamforming and power allocation to maximize the instantaneous data rate, under both the individual and the sum power constraints over the source and relay nodes. As the problem is non-convex, we propose an efficient sub-optimal solution based on block-coordinate descent method by decomposing the problem into two sub-problems: a beamforming optimization sub-problem with given power allocation and a power allocation sub-problem with fixed beamforming. For the beamforming design sub-problem, the optimal solution is obtained based on the semi-definite relaxation technique. For the power allocation sub-problem, the optimal solution is obtained in closed form. Then, the closed-form cumulative distribution function and outage probability expressions for sub-optimal beamforming with both uniform power allocation and optimal power allocation are derived. In addition, simple and informative high signal-to-noise ratio (SNR) approximations for outage probability expressions are presented to gain insights. Finally, the optimal flying altitude that minimizes the average outage probability is obtained via one-dimensional search.

Stochastic Energy Harvesting and Relay Selection for Two-Way Dual-Relay Networks

The optimal transmission policy is investigated for two-way (TW) energy harvesting (EH) dual-relay networks with a stochastic EH model. There exist two solar-powered relays which have finite-sized batteries, and at most one relay is selected to facilitate the network’s information exchange. The objective is to optimize the network’s long-term outage performance by adapting the relay selection and power allocation to the system stochastic conditions. Thus, a Markov decision process (MDP) is utilized to formulate the design framework. From the MDP, the optimal transmission policy of the network is derived to indicate the adaptive relay selection and power allocation, and the computation of the network’s expected outage probability is presented. Further, the optimal transmission policy is asymptotically analyzed, and an active relaying property and a power convergency property of the optimal policy are pointed out. Moreover, according to the properties of the optimal policy, the long-term outage probabilities of the network are found to approach the limitation in sufficiently high SNRs, which is determined by the empty probability of the dual-battery under the optimal policy. Finally, computer simulations validate the analysis.

Joint relay selection and power allocation protocol for two-way relaying system

The Joint Relay Selection and Power Allocation (JRSPA) for Amplify-and-Forward (AF) relaying (JRSPA-AF) combines Power Allocation (PA) and Relay Selection (RS) very well in Two-Way Relaying (TWR) system. In order to explore the system performance of JRSPA-AF protocol, the outage probability and Diversity-Multiplexing Trade-off (DMT) of JRSPA-AF protocol are analysed. One upper bound, two lower bounds and approximate expressions of outage probability along with the DMT are derived. Based on this, the idea of JRSPA is introduced into Decode-and-Forward (DF) relaying TWR system, and the JRSPA-DF protocol is proposed. Moreover, the exact expressions of outage probability and DMT for JRSPA-DF are derived. Simulations are developed to validate the analytical results, the results show that the approximation outage probability of JRSPA-AF can be used well to evaluate system outage probability over the entire transmission SNR region, and the analysis results of outage probability of JRSPA-DF are consistent with its simulation results. In addition, the simulation results indicate that JRSPA-DF can achieve lower outage probability than JRSPA-AF and other protocols.

Joint Relay Selection and Power Allocation for the Physical Layer Security of Two-Way Cooperative Relaying Networks

In this paper, we investigate the physical layer security of cooperative two-way relay transmission systems using the amplify-and-forward (AF) protocol in the presence of an eavesdropper. A joint relay selection (RS) and power allocation (PA) scheme is proposed to protect the source-destination transmission against the eavesdropper. However, due to the high computational complexity, it is difficult to obtain the optimal solution for the system secrecy rate. Fortunately, an approximate optimal solution by using the particle swarm optimization (PSO) algorithm is derived. In the simulations, we use random relay selection with optimal power allocation (RRS-OPA) and equal power allocation with optimal relay selection (EPA-ORS) as benchmark schemes to verify the effectiveness of the proposed method. The simulation results show that the proposed method outperforms both RRS-OPA and EPA-ORS and significantly improves the system performance with low complexity.