MARL-Based Access Control for Grant-Free Non-Orthogonal Random Access in UDN

This study addresses the challenge of high power collision rates in Grant-Free Non-Orthogonal Random Access (GF-NORA) for ultra-massive machine-type communication (umMTC) in ultra-dense networks (UDN). We analyze the impact of power collision and inter-cell interference, defining the key factors affecting successive interference cancellation (SIC) decoding failure. To tackle power collision problem, we propose a multi-agent reinforcement learning (MARL) framework, QMIX algorithm, with joint optimization of access control and power-level design. We evaluate the performance of the proposed scheme with extensive random access simulations in an umMTC environment. Our approach outperforms state-of-the-art schemes, achieving at most 10% increase in successful SIC decoding rate with lower access delay.