CSI-Free Gamma CDF-Based Distributed User Scheduling for IRS-Aided Multi-User Systems

Abstract

In this paper, we propose a CSI-free distributed user scheduling scheme for intelligent reflecting surface (IRS)-aided multi-user systems. Building upon adaptive conditional sample mean (A-CSM), originally developed for blind beamforming in IRS-aided systems, we exploit the first-stage A-CSM output as a local scheduling metric without explicit channel state information (CSI). The obtained metric exhibits a non-negative and rightskewed distribution, which can be effectively approximated by a Gamma distribution. Based on this observation, the proposed CSI-free-D-Gamma scheme first maps the local metric into a normalized access variable using a moment-matched Gamma CDF. Then, unlike LUT- and Uniform-based baselines that terminate with equal-width slot-region mapping, the proposed Gamma scheme further adjusts the discrete slot-index regions in the normalized domain by considering both the Gammainduced rate contribution and the earliest-singleton collision behavior. Under the considered symmetric small-scale fading setting, numerical results show that the proposed CSI-free-D-Gamma scheme reduces the collision probability and improves the average achievable rate compared with LUT-based, fixed minmax uniform mapping, and Random Scheduling baselines.