Action Elimination-assisted Deep Reinforcement Learning for B5G Cell Selection and Network Slicing

Sunwoo Kim, Seungnyun Kim, Kyungjoo Suh, Byonghyo Shim

Abstract: With the emergence of 5G era, network slicing has received much attention due to its ability to support various services. Network slicing is an approach to partition a single physical network into multiple slices supporting separate services and has been extended to the handover scenario (where the UE moves from one cell to another) recently. In this paper, we propose a deep reinforcement learning (DRL)-based handover-aware network slicing technique for the cell selection and network slicing. Key ingredient of the proposed technique is to use action elimination to reduce the size of slice allocation decision space. In our work, we first determine the target cell providing the maximum user-requested services to the handover UE, and then assign network slices to the handover UE by exploiting action elimination-assisted DRL. From the numerical results, we demonstrate that the proposed technique outperforms the conventional network slicing techniques in terms of throughput.