Multi-Connectivity Mobility Management in Downlink FD-RAN: A Learning Based Approach

We consider a fully-decoupled radio access network (FD-RAN), where base stations (BSs) are physically decoupled into control BSs, uplink BSs and downlink BSs, and multi-connectivity becomes the default user equipment (UE) association mode. Specifically, we study the inter-frequency multi-connectivity in downlink of FD-RAN and present a deep reinforcement learning based online multi-connectivity mobility management scheme. We formulate a UE dynamic multiple access problem and transform it into a handover decision problem, then apply the double deep Q-network (DDQN) algorithm to make real time mobility management decisions. Simulation results show that the proposed scheme outperforms benchmarks in terms of handover frequency and quality of service, while ensuring real-time performance.