Deep Reinforcement Learning-Based Policy for Baseband Function Placement and Routing of RAN in 5G and Beyond

In this paper, we propose a deep reinforcement learning (DRL)-based algorithm to generate policies of Baseband Function (BBF) placement and routing. order explore the performance proposed in practical systems, online scenario with completely random requests is used simulation considering C-RAN NG-RAN architectures. Besides, an Integer Linear Programming (ILP) model formulated optimal solution as benchmark. The results show that DRL-based converges short time, its closes benchmark obtained by ILP terms latency bandwidth for scenarios. addition, generated based on DRL compared classic heuristic algorithm, i.e., first-fit algorithm. superior from above two perspectives. fast convergence near-optimal prove promising approach BBF routing RAN 5G Beyond.