Game Theoretic Reinforcement Learning for Mobility-Aware Resource Allocation in 5G MIMO

Next-generation 5G networks with massive Multiple Input Multiple Output (MIMO) must efficiently allocate radio resources to mobile users whose channel conditions change rapidly due to movement. This paper proposes a novel game-theory Reinforcement Learning (RL) framework for mobility-aware resource allocation in 5G MIMO systems. We model the resource allocation problem as a dynamic game between network entities and integrate a predictive deep RL agent that anticipates User Equipment (UE) mobility patterns. By forecasting UE movement, the RL agent proactively assists a game-theory optimization of MIMO resource allocation before channel quality degrades. The combination of game theory with predictive RL enables the network to reach a near-equilibrium resource distribution that is both adaptive and fair, improving convergence stability compared to standalone learning or game approaches. Simulation results in a high-mobility 5G scenario demonstrate that the proposed approach significantly boosts user Quality of Service (QoS) for example, increasing average throughput and reducing latency and handover failures relative to conventional reactive allocation strategies. Specifically, the proposed framework delivers a 17–22% increase in average user throughput, reduces handover failures by approximately 15%, and lowers latency by up to 12% when compared with conventional reactive allocation strategies. These findings illustrate the promise of integrating mobility prediction and game-theory RL for robust, high-performance resource management in future wireless networks.