Three High-Rate Beamforming Methods for Active IRS-Aided Wireless Network
Feng Shu, Jing Liu, Yeqing Lin, Yang Liu, Zhilin Chen, Xuehui Wang, Rongen Dong, Jiangzhou Wang
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 6 G 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.