A Deep Reinforcement Learning Framework for Clustered Cell-Free Networking Over User Mobility

Clustered cell-free networking paves a new way for enabling joint transmission among access points (APs) by decomposing the whole network into non-overlapping subnetworks. Previous works adopted clustering algorithms or conventional optimization theories to group users and APs into subnetworks, which usually require high computational costs and have long running time. In this paper, we leverage deep reinforcement learning (DRL) for clustered cell-free networking so as to rapidly adapt to user movement in dynamic scenarios. To effectively reduce the joint processing complexity of each subnetwork, we aim to maximize the balance of the subnetworks in addition to the sum rate. With such an objective, we propose a novel deep deterministic policy gradient based clustered cell-free networking (DDPG-C<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>F) framework. It is worth mentioning that the proposed framework requires much lower channel measurement overhead and computational complexity compared to the conventional approaches. Simulation results demonstrate the superior performance of the proposed DDPG-C<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>F framework in various scenarios.