Handover-Free Multi-Connectivity Mobility Management for Downlink FD-RAN: A Hierarchical DRL-Based Approach

Abstract

Seamless connectivity and on-demand service provision are considered as the fundamental capabilities in next-generation mobile networks (6G). However, current configuration of single base station (BS) connection and increasingly denser BS deployment pose great challenges for mobile user equipment (UE), due to the frequent handover and limited communication serving capacity. To this end, we investigate the handover-free multi-connectivity mobility management problem in downlink over a novel 6G architecture, namely fully-decoupled radio access network (FD-RAN). Particularly, we formulate the problem as a two-layer task involving UE-BS association and link power control, whose objective is to minimize the long-term absolute difference between UE’s serving rate and rate demand. We propose a hierarchical deep reinforcement learning (HDRL)-based scheme to decompose the original problem into two subproblems for efficient resolution. Specifically, a double deep Q-network (DDQN) algorithm is employed to update the multi-connectivity BS cooperation set for each UE at the first layer of HDRL. Then at the second layer, we design a transformer-assisted soft actor-critic (TSAC) algorithm to jointly determine transmission power for all links associated with each UE. Extensive simulations validate the effectiveness of proposed scheme over benchmarks, which is capable of providing seamless connectivity and fine-grained on-demand service for mobile UEs.