Robust Beamforming Design in a NOMA Cognitive Radio Network Relying on SWIPT

TL;DR

This paper develops a robust beamforming and power splitting design for a NOMA cognitive radio network with SWIPT, addressing CSI uncertainties and non-linear energy harvesting to improve power efficiency and energy harvesting performance.

Contribution

It introduces a joint robust beamforming and power splitting scheme for NOMA cognitive radio networks considering realistic CSI errors and non-linear energy harvesting models.

Findings

Proposed scheme outperforms traditional OMA methods.

Robust design effectively handles CSI uncertainties.

Gaussian CSI error model yields better performance.

Abstract

This paper studies a multiple-input single-output non-orthogonal multiple access cognitive radio network relying on simultaneous wireless information and power transfer. A realistic non-linear energy harvesting model is applied and a power splitting architecture is adopted at each secondary user (SU). Since it is difficult to obtain perfect channel state information (CSI) in practice, instead either a bounded or gaussian CSI error model is considered. Our robust beamforming and power splitting ratio are jointly designed for two problems with different objectives, namely that of minimizing the transmission power of the cognitive base station and that of maximizing the total harvested energy of the SUs, respectively. The optimization problems are challenging to solve, mainly because of the non-linear structure of the energy harvesting and CSI errors models. We converted them into convex forms by using semi-definite relaxation. For the minimum transmission power problem, we obtain the rank-2 solution under the bounded CSI error model, while for the maximum energy harvesting problem, a two-loop procedure using a one-dimensional search is proposed. Our simulation results show that the proposed scheme significantly outperforms its traditional orthogonal multiple access counterpart. Furthermore, the performance using the gaussian CSI error model is generally better than that using the bounded CSI error model.