MARL-Based Access Control for Grant-Free Nonorthogonal Random Access in UDN

Abstract

This study addresses the challenge of high-power collision rates in grant-free nonorthogonal random access (GF-NORA) for ultramassive machine-type communication (umMTC) in ultradense networks (UDNs). We analyze the impact of power collision and intercell interference, defining the key factors affecting successive interference cancelation (SIC) decoding failure. To tackle power collision problem, we propose a multiagent 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.