Research on joint game theory and multi-agent reinforcement learning-based resource allocation in micro operator networks

Local 5G networks represent an emerging architecture in the 5G architecture, where local micro operators (MOs) reuse public mobile networks to support differentiated service transmission capacity and coverage requirements. 5G Radio Access Network (RAN) slicing technology offers a solution that allows for the flexible deployment of heterogeneous services as slices sharing the same infrastructure. In this paper, a multi-micro operators scenario with deployed RAN slicing is considered, where interference price is used to incentivize local micro operators to optimize their transmission power and resource block allocation strategies, reducing interference to mobile network users and enhancing transmission efficiency. We formulate the competitive interaction between mobile network operator (MNO) and local MOs as a two-stage Stackelberg game, with the MNO as the leader and MOs as followers. The MNO is responsible for establishing the interference price that MOs need to pay for their communication. MOs decide on their transmission power strategies to satisfy user-customized slice requirements by solving the game. A resource management scheme based on multi-agent reinforcement learning is proposed, introducing a game-theoretic equilibrium solution to determine resource block allocation strategies, ensuring slice isolation while increasing operator revenue. Experimental results demonstrate that our approach outperforms standalone reinforcement learning strategies in terms of transmission rates and interference price payments.