Model-Free Learning of Optimal Beamformers for Passive IRS-Assisted Sumrate Maximization | IEEE Conference Publication | IEEE Xplore

Model-Free Learning of Optimal Beamformers for Passive IRS-Assisted Sumrate Maximization


Abstract:

Although Intelligent Reflective Surfaces (IRSs) are a cost-effective technology promising high spectral efficiency in future wireless networks, obtaining optimal IRS beam...Show More

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.
Date of Conference: 04-10 June 2023
Date Added to IEEE Xplore: 05 May 2023
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Conference Location: Rhodes Island, Greece

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