Throughput Optimization for NOMA Cognitive Relay Network with RF Energy Harvesting Based on Improved Bat Algorithm

Abstract

Due to the shortcomings of the standard bat algorithm (BA) for multi-parameter optimization, an improved bat algorithm is proposed. The benchmark function test shows that the proposed algorithm has better realization of high-dimensional function optimization by introducing multiple flight modes, adopting adaptive strategy based on group trend, and employing loudness mutation flight selection strategy based on Brownian motion. Aiming at the characteristics of complex networks structure and multiple design variables of energy harvesting non-orthogonal multiple access cognitive relay networks (EH-NOMA-CRNs), we utilize the proposed hybrid strategy improved bat algorithm (HSIBA) to optimize the performance of EH-NOMA-CRNs. At first, we construct a novel two-hop underlay power beacon assisted EH-NOMA-CRN, and derive the closed-form expressions of secondary network’s outage probability and throughput. Then, the secondary network performance optimization is formulated as the throughput maximation problem with regard to EH ratio and power allocation factors. Subsequently, the HSIBA is employed to optimize the above parameters. Numerical results show that the proposed HSIBA can achieve optimization to the constructed EH-NOMA-CRN with faster convergence speed and higher stability.