Three High-rate Beamforming Methods for Active IRS-aided Wireless Network

TL;DR

This paper introduces three high-rate beamforming methods for active IRS in wireless networks, significantly improving data rates over existing techniques by leveraging closed-form solutions and iterative optimization.

Contribution

It proposes two novel high-rate beamforming methods, MRR and SRR, with closed-form expressions, and a Max-ASNR method that iteratively enhances rate performance.

Findings

Max-ASNR outperforms MRR, SRR, and EGR in rate.

All three proposed methods outperform existing EGR.

Simulation results confirm substantial rate improvements.

Abstract

Due to its ability of breaking the double-fading effect experienced by passive intelligent reflecting surface (IRS), active IRS is evolving a potential technique for future 6G wireless networks. To fully exploit the amplifying gain achieved by active IRS, two high-rate methods, maximum ratio reflecting (MRR) and selective ratio reflecting (SRR) are presented, which are motivated by maximum ratio combining and selective ratio combining. Moreover, both MRR and SRR are in closed-form expressions. To further improve the rate, a maximum approximate-signal-to-noise ratio (Max-ASNR) is first proposed with an alternately iterative infrastructure between adjusting the norm of beamforming vector and its normalized vector. This may make a substantial rate enhancement over existing equal-gain reflecting (EGR). Simulation results show that the proposed three methods perform much better than existing method EGR in terms of rate. They are in decreasing order of rate performance: Max-ASNR, MRR, SRR, and EGR.