Intelligent reflecting surface aided wireless networks-Harris Hawks optimization for beamforming design

TL;DR

This paper introduces a novel Harris Hawks Optimizer-based approach to optimize beamforming in IRS-assisted wireless networks, demonstrating improved signal power and efficiency over existing methods.

Contribution

It is the first to apply a meta-heuristic algorithm, specifically HHO, to optimize IRS-aided wireless network beamforming, addressing a non-convex problem effectively.

Findings

HHO achieves comparable or better results than other algorithms.

The proposed method effectively maximizes received signal power.

Simulation confirms the feasibility and efficiency of the HHO-based scheme.

Abstract

Intelligent Reflecting Surface (IRS) is envisioned to be a promising green and cost-effective solution to enhance wireless network performance by smartly reconfiguring the signal propagation. In this paper, we study an IRS-aided multiple-input single-output wireless network where a multi-antenna Access Point (AP) services a single-antenna user assisted by an IRS. The goal is to maximize the received signal power by jointly optimizing the transmit beamforming at the AP and the reflection coefficient at the IRS. The formulated optimization problem is non-convex and subject to constraints. We adopt a novel nature-inspired optimization technique named Harris Hawks Optimizer (HHO) to tackle the problem. After transforming the constrained problem into an unconstrained problem using penalty method, the formulated problem is optimized by the HHO. To the best of our knowledge, this is the first time to use a meta-heuristic algorithm to solve the IRS-aided network optimization problem. Simulation is conducted to verify the feasibility of the HHO-based scheme. The results show that the HHO-based scheme could provide similar or even better optimization results compared with other optimization algorithms.