Robust Beamforming for SWIPT System with Chance Constraints

TL;DR

This paper develops a robust beamforming approach for SWIPT systems with chance constraints, optimizing sum rate under imperfect CSI and energy harvesting uncertainties, using Bernstein inequality and AO algorithm.

Contribution

It introduces a novel robust beamforming method for SWIPT with chance constraints, transforming the problem into deterministic form and solving via an AO algorithm.

Findings

Achieves higher sum rate compared to non-robust schemes

Effectively handles CSI imperfections and energy constraints

Demonstrates the effectiveness of the Bernstein inequality approach

Abstract

The robust beamforming problem in multiple-input single-output (MISO) downlink networks of simultaneous wireless information and power transfer (SWIPT) is studied in this paper. Adopting the time switching fashion to perform energy harvesting and information decoding respectively, we aim at maximizing the sum rate under imperfect channel state information (CSI) and the chance constraints of users' harvested energy. In view of the fact that the constraints for minimal harvested energy is not necessary to meet from time to time, this paper adopts chance constraint to model it and uses the Bernstein inequality to transform it into deterministic constraints equivalently. Recognizing the maximum sum rate problem of imperfect CSI as nonconvex problem, we transform it into finding the expectation of minimum mean square error (MMSE) equivalently in this paper, and an alternative optimization (AO) algorithm is proposed to decompose the optimization problem into two sub-problems: the transmit beamformer design and the division of switching time. The simulation results show the performance gains compared to non-robust state of the art schemes.