# A Low-Complexity Solution to Sum Rate Maximization for IRS-assisted SWIPT-MIMO Broadcasting

## TL;DR

This paper proposes a low-complexity algorithm for maximizing the weighted sum rate in IRS-assisted SWIPT-MIMO systems, effectively decoupling variables and nearly doubling performance with linear complexity growth.

## Contribution

It introduces a novel penalty dual decomposition and gradient projection method to efficiently optimize IRS-assisted SWIPT-MIMO systems, outperforming existing benchmarks.

## Key findings

- Nearly doubles the weighted sum rate compared to benchmarks
- Complexity grows linearly with IRS elements, unlike cubic growth in benchmarks
- Achieves a stationary solution efficiently in a challenging non-convex problem

## Abstract

This paper focuses on the fundamental problem of maximizing the achievable weighted sum rate (WSR) at information receivers (IRs) in an intelligent reflecting surface (IRS) assisted simultaneous wireless information and power transfer system under a multiple-input multiple-output (SWIPT-MIMO) setting, subject to a quality-of-service (QoS) constraint at the energy receivers (ERs). Notably, due to the coupling between the transmit precoding matrix and the passive beamforming vector in the QoS constraint, the formulated non-convex optimization problem is challenging to solve. We first decouple the design variables in the constraints following a penalty dual decomposition method, and then apply an alternating gradient projection algorithm to achieve a stationary solution to the reformulated optimization problem. The proposed algorithm nearly doubles the WSR compared to that achieved by a block-coordinate descent (BCD) based benchmark scheme. At the same time, the complexity of the proposed scheme grows linearly with the number of IRS elements while that of the benchmark scheme is proportional to the cube of the number of IRS elements.