Max-Min Rate Optimization for Uplink IRS-NOMA With Receive Beamforming

Abstract

This letter addresses an intelligent reflecting surface (IRS) to the uplink nonorthogonal multiple access (NOMA) served by a multiantenna receiver for more efficient data collection from massive devices. For rate fairness, we formulate a problem of maximizing the minimum rate of the devices by optimizing receive beamforming (BF), IRS reflection, and transmit power allocation (PA) of the devices jointly. We first design a block coordinate descent (BCD) algorithm optimizing receive BF, IRS reflection, and PA iteratively. We then reformulate the problem as a nonlinear optimization (NLO) problem with a smooth objective function of the IRS phase and PA vectors by incorporating the optimal receive BF into the objective and using an approximate minimum. To handle massive IRS elements and devices efficiently, we solve the NLO problem with the limitedmemory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm using the gradient derived in a closed form. The results show that the L-BFGS-B optimizing the IRS phase and PA vectors concurrently reduces the computational complexity of the BCD algorithm significantly at a slight performance gain.