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

TL;DR

This paper introduces a deep reinforcement learning framework for clustered cell-free networking that adapts quickly to user mobility, reduces measurement costs, and outperforms existing methods across various scenarios.

Contribution

The paper proposes a novel DRL-based framework (DDPG-C2F) for dynamic clustered cell-free networking, significantly reducing measurement costs and improving adaptability and performance.

Findings

Outperforms existing baselines in multiple scenarios

Reduces handover costs in mobile environments

Maintains robustness in dynamic user scenarios

Abstract

Clustered cell-free networking paves a new way for enabling scalable joint transmission among access points (APs) by partitioning the whole network into non-overlapping subnetworks. Previous works adopted clustering algorithms, graph partitioning methods or conventional continuous optimization theories to partition a network based on the channels between all users and all APs, resulting in huge channel measurement and computational costs. This makes these methods difficult to be implemented in practical systems since the optimal network partition could vary frequently due to user mobility. In addition, existing methods were usually designed for specific clustered cell-free networking problems with different optimization algorithms employed. In this paper, we leverage deep reinforcement learning (DRL) for clustered cell-free networking so as to rapidly adapt to user movements in dynamic environments, and propose a deep deterministic policy gradient based clustered cell-free networking (DDPG-C$^{2}$F) framework that can be adapted in various application scenarios. Moreover, in our framework, only one single channel needs to be estimated at each AP as the input of the neural network, which greatly reduces the channel measurement costs for clustered cell-free networking, and the training and inference costs of our framework. The proposed DDPG-C$^{2}$F framework is then applied to various clustered cell-free networking problems with different objectives and constraints to demonstrate its performance. Simulation results show that our framework outperforms existing baselines in all scenarios. Moreover, we show that the proposed framework can reduce the handover cost over user mobility, and is robust to dynamic scenarios with random user joining or leaving.