Enhanced-rate Iterative Beamformers for Active IRS-assisted Wireless Communications

Compared to passive intelligent reflecting surface (IRS), active IRS is viewed as a more efficient promising technique to combat the double-fading impact in IRS-aided wireless network. In this paper, in order to boost the achievable rate of user in such a wireless network, three enhanced-rate iterative beamforming methods are proposed by designing the amplifying factors and the corresponding phases at active IRS. The first method, called generalized maximum ratio reflection (GMRR), is presented with a closed-form expression, which is motivated by the maximum ratio combing. To further improve rate, maximize the simplified signal-to-noise ratio (Max-SSNR) is designed by omitting the cross-term in the definition of rate. Using the Rayleigh-Ritz (RR) theorem and the fractional programming (FP), two enhanced methods, Max-SSNR-RR and Max-SSNR-FP are proposed to iteratively optimize the norm of beamforming vector and its associated normalized vector. Simulation results indicate that the proposed three methods make an obvious rate enhancement over Max-reflecting signal-to-noise ratio (RSNR) and passive IRS, and are in increasing order of rate performance as follows: GMRR, Max-SSNR-RR, and Max-SSNR-FP.