Relay Power Allocation Problem Research Articles

Energy Efficiency for SWIPT in MIMO Two-Way Amplify-and-Forward Relay Networks

In this paper, we study the energy efficiency (EE) optimization for simultaneous wireless information and power transfer in multiple-input-multiple-output two-way amplify-and-forward relay networks, where the energy-constrained relay harvests energy from two sources’ signals by power splitting (PS) scheme to forward the information of two sources. Our objective is to jointly design the sources and relay precoding matrices and the PS ratio to maximize the EE of the network, under the transmit power constraints at sources and relay and the minimum spectral efficiency requirement. We transform the nonconvex precoding and PS design problem into a difference of convex programming and propose an efficient iterative algorithm based on the constrained concave convex procedure to obtain a local optimum. To reduce computational complexity, we propose a generalized singular value decomposition based channel diagonalization scheme to simplify the optimization into a joint source and relay power allocation problem. The alternating optimization method is employed to solve this problem and a suboptimal solution to the EE maximization problem is obtained. Simulation results demonstrate the effectiveness of the proposed schemes.

Joint Source and Relay Secure Beamforming for Nonregenerative MIMO Relay Systems With Wireless Information and Power Transfer

Simultaneous wireless information and power transfer (SWIPT) is able to prolong the lifetime of energy-constrained wireless networks. In this paper, we study the security issue of a nonregenerative multiple-input multiple-out (MIMO) relay system with SWIPT. When the full channel state information (CSI) of the eavesdropper is available, we aim to maximize the secrecy rate under the source and relay transmit power constraints and energy-harvesting (EH) constraints at the multiple EH receivers by jointly designing the source and relay beamforming matrices, which is a nonconvex optimization and is still an open problem. In order to enhance the secrecy rate effectively, we first transform the optimization into a difference of convex programming and propose a sequential parametric convex approximation-based iterative algorithm which is able to achieve a local optimum. To reduce the complexity, we also propose a singular value decomposition (SVD)-generalized SVD-based method to simplify the optimization into a joint source and relay power allocation problem. The alternating optimization is employed to find a suboptimal solution of the joint power allocation problem. When the CSI of the eavesdropper is unknown, we propose an artificial-noise (AN) aided joint source and relay beamforming scheme, which can maximize the AN power to jam the eavesdropper under the limited source and relay transmit power and satisfy the quality of service of the receiver. Simulation results have shown the effectiveness of our proposed schemes.

State‐based potential power allocation game in a cooperative multiuser network

Multiuser cooperative communications represent a research area of great interest constituting a key technology for future wireless networks. In this study, the authors consider a scenario of multi-user, single-relay network, where each user employs a part of the relay's transmission power taking into account the constraint of the total transmission power of the relay. The problem of relay power allocation to individual users with a view to optimising the long-term network performance is investigated. An adequate utility function is defined, in terms of outage probability, evaluating the cooperative network's performance due to the relay's cooperation. Taking into consideration the distributed nature of cooperative networks along with the coupled constraint of relay's total transmission power, the specific problem is transformed from a network-level to a user-level optimisation problem using a modern game-theoretic tool. More specifically, the initial power allocation problem is formulated as a state-based potential game for which the corresponding Nash equilibrium exists. Finally, a gradient learning mechanism is proposed to achieve the stationary state Nash equilibrium of the game that constitutes also the global optimal solution of the initial problem. The performance of the new proposed scheme is investigated through numerical simulations for various network parameters.

Price-based resource allocation for revenue maximization with cooperative communication

In recent years, cooperative communication becomes a promising technology to improve the spatial diversity for the future mobile network. Under this communication paradigm, both relay assignment and power allocation will greatly impact the network performance. However, since each device may be selfish, a significant challenge is to make the joint resource allocation robust to node selfishness while pursuing the maximum system revenue in the commercial network. This paper mainly studies the price-based joint relay-power allocation problem which aims to maximize the revenue (JRP-RM) while satisfying the bandwidth requirement with cooperative communication. We first analyze the power allocation under the bandwidth and selfishness constraints for different communication modes. According to the analytical results, this paper presents a polynomial-time algorithm, namely MRPA, to solve the JRP-RM problem. We prove its correctness and show that it can achieve an approximation ratio of $$1 - \varepsilon$$ , where $$\varepsilon$$ is an arbitrarily positive constant. Moreover, we study the JRP-RM problem under a special case where the cost and payment are both linear functions of the transmission power. The simulation results show that MRPA can increase the system revenue about 34.2 and 18.9 % compared with the OPRA Yang et al. (2011 IEEE International Conference on Communications (ICC). IEEE, pp 1–6, 2011) and JRPA Xu et al. (Wirel Netw pp 2209–2219, 2010) algorithms while satisfying the selfishness constraint in most situations.

A Dual-Phase Power Allocation Scheme for Multicarrier Relay System With Direct Link

We present optimization algorithms for source and relay power allocations in a multicarrier relay system with direct link, where the source power is allowed to transmit in both phases in a two-phase relay scheme. We show that there is a significant benefit to the system capacity by allowing the source power to be distributed over both phases. Specifically, we consider the joint optimization of source and relay power to minimize a general cost function. The joint optimization problem is non-convex and the complexity of finding the optimal solution is extremely high. Using the alternating optimization (AO) method, the joint problem is decomposed into a convex source power allocation problem and a non-convex relay power allocation problem. By exploiting the specific structure of the problem, we present efficient algorithms that yield the exact optimal solutions for both source and (non-convex) relay power allocation problems. Then we show that the overall AO algorithm converges to a stationary point of the joint problem. Moreover, the proposed AO algorithm is asymptotically optimal for large relay transmit power or large source-relay channel gain. Finally, simulations show that the proposed AO algorithm achieves significant gain over various baselines.

Throughput Maximization for the Gaussian Relay Channel with Energy Harvesting Constraints

This paper considers the use of energy harvesters, instead of conventional time-invariant energy sources, in wireless cooperative communication. For the purpose of exposition, we study the classic three-node Gaussian relay channel with decode-and-forward (DF) relaying, in which the source and relay nodes transmit with power drawn from energy-harvesting (EH) sources. Assuming a deterministic EH model under which the energy arrival time and the harvested amount are known prior to transmission, the throughput maximization problem over a finite horizon of N transmission blocks is investigated. In particular, two types of data traffic with different delay constraints are considered: delay-constrained (DC) traffic (for which only one-block decoding delay is allowed at the destination) and no-delay-constrained (NDC) traffic (for which arbitrary decoding delay up to N blocks is allowed). For the DC case, we show that the joint source and relay power allocation over time is necessary to achieve the maximum throughput, and propose an efficient algorithm to compute the optimal power profiles. For the NDC case, although the throughput maximization problem is non-convex, we prove the optimality of a separation principle for the source and relay power allocation problems, based upon which a two-stage power allocation algorithm is developed to obtain the optimal source and relay power profiles separately. Furthermore, we compare the DC and NDC cases, and obtain the sufficient and necessary conditions under which the NDC case performs strictly better than the DC case. It is shown that NDC transmission is able to exploit a new form of diversity arising from the independent source and relay energy availability over time in cooperative communication, termed "energy diversity", even with time-invariant channels.

Robust Power Allocation Algorithms for Wireless Relay Networks

Resource allocation promises significant benefits in wireless networks. In order to fully reap these benefits, it is important to design efficient resource allocation algorithms. Here, we develop relay power allocation (RPA) algorithms for coherent and noncoherent amplify-and-forward (AF) relay networks. The goal is to maximize the output signal-to-noise ratio under individual as well as aggregate relay power constraints. We show that these RPA problems, in the presence of perfect global channel state information (CSI), can be formulated as quasiconvex optimization problems. In such settings, the optimal solutions can be efficiently obtained via a sequence of convex feasibility problems, in the form of second-order cone programs. The benefits of our RPA algorithms, however, depend on the quality of the global CSI, which is rarely perfect in practice. To address this issue, we introduce the robust optimization methodology that accounts for uncertainties in the global CSI. We show that the robust counterparts of our convex feasibility problems with ellipsoidal uncertainty sets are semi-definite programs. Our results reveal that ignoring uncertainties associated with global CSI often leads to poor performance, highlighting the importance of robust algorithm designs in practical wireless networks.