Knowledge Transfer-Based Multiagent Q-Learning for Medium Access in Dense Cellular Networks

Inter-cell interference in dense cellular networks causes severe network performance degradation because the base stations (BSs) and devices cannot fully utilize all the network status information in practice. Reinforcement learning enables the wireless network to find an effective transmission strategy with limited information and the network entities to operate in a distributed manner. We herein propose a medium access algorithm based on multi-agent reinforcement learning by exchanging knowledge of transmissions from other cells. Specifically, a state mapping function between the device and the BS and a transfer value iteration are proposed. The BS transfers reward processes derived from inter-cell interference so that the partially observed medium of devices is expanded to monitor entire networks. Extensive simulation results show that simple knowledge transfer can help performance improvement in terms of network stability and throughput.