CA‐PPO: Collision‐Aware Reinforcement Learning for Grant‐Free NOMA in Wireless Federated Learning

This paper proposes a collision‐aware proximal policy optimisation (CA‐PPO) framework for adaptive resource management in grant‐free nonorthogonal multiple access (GF‐NOMA) enabled wireless federated learning systems. Unlike conventional approaches that rely on static clustering or grant‐based access, CA‐PPO dynamically coordinates device clustering and transmit‐power allocation under stochastic channel conditions, random‐access collisions and successive interference cancelation (SIC) constraints. The proposed framework employs a hybrid discrete–continuous actor–critic architecture with a collision‐aware reward that jointly captures decoding success, energy consumption, latency and retransmissions. CA‐PPO enables scalable and reliable aggregation in ultradense IoT deployments by improving participation stability and reducing communication overhead. Simulation results demonstrate that the proposed approach accelerates convergence by approximately 16%, reduces per‐device energy consumption by up to 25% and sustains high device participation under dense connectivity. These findings highlight the potential of learning‐driven network‐aware resource optimisation for next‐generation wireless federated learning systems.