Hierarchical Multi Agent DRL for Soft Handovers Between Edge Clouds in Open RAN

Abstract

Multi-connectivity (MC) for aerial users via a set of ground access points offers the potential for highly reliable communication. Within an open radio access network (O-RAN) architecture, edge clouds (ECs) enable MC with low latency for users within their coverage area. However, ensuring seamless service continuity for transitional users-those moving between the coverage areas of neighboring ECs-poses challenges due to centralized processing demands. To address this, we formulate a problem facilitating soft handovers between ECs, ensuring seamless transitions while maintaining service continuity for all users. We propose a hierarchical multi-agent reinforcement learning (HMARL) algorithm to dynamically determine the optimal functional split configuration for transitional and non-transitional users. Simulation results show that the proposed approach outperforms the conventional functional split in terms of the percentage of users maintaining service continuity, with at most 4% optimality gap. Additionally, HMARL achieves better scalability compared to the static baselines.