Robust Weighted Sum Harvested Energy Maximization for SWIPT Cognitive Radio Networks Based on Particle Swarm Optimization

Pham Viet Tuan; Insoo Koo

doi:10.3390/s17102275

Robust Weighted Sum Harvested Energy Maximization for SWIPT Cognitive Radio Networks Based on Particle Swarm Optimization

Sensors (Basel). 2017 Oct 6;17(10):2275. doi: 10.3390/s17102275.

Authors

Pham Viet Tuan^{1

2}, Insoo Koo³

Affiliations

¹ School of Electrical and Computer Engineering, University of Ulsan, Ulsan 680-749, Korea. phamviettuan@gmail.com.
² Faculty of Physics, University of Education, Hue University, 34 Le Loi Str., Hue City 530000, Vietnam. phamviettuan@gmail.com.
³ School of Electrical and Computer Engineering, University of Ulsan, Ulsan 680-749, Korea. iskoo@ulsan.ac.kr.

Abstract

In this paper, we consider multiuser simultaneous wireless information and power transfer (SWIPT) for cognitive radio systems where a secondary transmitter (ST) with an antenna array provides information and energy to multiple single-antenna secondary receivers (SRs) equipped with a power splitting (PS) receiving scheme when multiple primary users (PUs) exist. The main objective of the paper is to maximize weighted sum harvested energy for SRs while satisfying their minimum required signal-to-interference-plus-noise ratio (SINR), the limited transmission power at the ST, and the interference threshold of each PU. For the perfect channel state information (CSI), the optimal beamforming vectors and PS ratios are achieved by the proposed PSO-SDR in which semidefinite relaxation (SDR) and particle swarm optimization (PSO) methods are jointly combined. We prove that SDR always has a rank-1 solution, and is indeed tight. For the imperfect CSI with bounded channel vector errors, the upper bound of weighted sum harvested energy (WSHE) is also obtained through the S-Procedure. Finally, simulation results demonstrate that the proposed PSO-SDR has fast convergence and better performance as compared to the other baseline schemes.

Keywords: cognitive radio networks (CRNs); particle swarm optimization (PSO); power-splitting, semidefinite relaxation (SDR); simultaneous wireless information and power transfer (SWIPT).