Low-Complexity Particle Swarm Optimization (PSO)-based Resource Allocation Scheme for a Cooperative Non-linear SWIPT-enabled NOMA System

Abstract

Cooperative networks integrating non-orthogonal multiple access (NOMA) and simultaneous wireless information power transfer (SWIPT) are emerging technologies that have been investigated as potential techniques to support the proliferation of the Internet of Things (IoT) and to obtain greener communications. In this sense, the optimization of resource allocation schemes is crucial to improve the performance of future cooperative wireless networks. However, conventional optimization methods that attempt to find the optimal solution may entail high computational complexity. Therefore, we propose a low-complexity particle swarm optimization (PSO)-based scheme to solve the resource allocation problem in a cooperative non-linear SWIPT-enabled NOMA system with a non-linear energy harvesting (EH) user. Specifically, we consider two optimization problems. First, we minimize transmission power, and second, we maximize energy efficiency subject to meeting quality-of-service (QoS) constraints. The problems are non-convex and challenging to solve. Furthermore, we develop the optimal solution based on convex optimization and the exhaustive search (ES) method to validate the results of the proposed PSO-based framework. Afterward, we investigate the performance of five swarm intelligence-based baseline schemes and evaluate an additional low-complexity solution based on the cuckoo search (CS) technique. For comparison purposes, we use orthogonal multiple access (OMA), equal power splitting (EPS), and time fixed (TF) baseline schemes. To our satisfaction, the proposed SWIPT NOMA network outperforms the benchmark schemes, and the proposed PSO-based framework achieves the nearest performance to the optimal scheme with lower complexity than obtained by the comparative swarm intelligence techniques and from convex optimization with the ES method.