Model-Free Learning of Optimal Beamformers for Passive IRS-Assisted Sumrate Maximization
Hassaan Hashmi, Spyridon Pougkakiotis, Dionysios S. Kalogerias
Abstract
Although Intelligent Reflective Surfaces (IRSs) are a cost-effective technology promising high spectral efficiency in future wireless networks, obtaining optimal IRS beamformers is a challenging problem with several practical limitations. Assuming fully-passive, sensing- free IRS operation, we introduce a new data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA) algorithm for sumrate optimization in an IRS-aided downlink setting. ZoSGA does not require access to channel model or network structure information, and enables learning of optimal long-term IRS beamformers jointly with standard short-term precoding, based only on conventional effective channel state information. Supported by state-of-the-art (SOTA) convergence analysis, detailed simulations confirm that ZoSGA exhibits SOTA empirical behavior as well, consistently outperforming standard fully model-based baselines, in a variety of scenarios.
BibTeX
@inproceedings{icassp2023_modelfreelearnin,
title = {Model-Free Learning of Optimal Beamformers for Passive IRS-Assisted Sumrate Maximization},
author = {Hassaan Hashmi and Spyridon Pougkakiotis and Dionysios S. Kalogerias},
booktitle = {ICASSP 2023},
year = {2023}
}