A Federated Deep Learning Framework for Cell-Free RSMA Networks

TL;DR

This paper introduces a federated deep learning framework for cell-free RSMA networks, combining multiple advanced wireless technologies to optimize network performance, security, and robustness through a novel distributed learning approach.

Contribution

It proposes a new federated deep reinforcement learning scheme for joint AP selection and precoder design in cell-free RSMA networks, integrating PCA for AP-UE assignment.

Findings

FDRL achieves comparable performance to centralized DRL.

The approach enhances security and reduces processing demands.

It effectively balances performance and distributed operation.

Abstract

Next-generation wireless networks are poised to benefit significantly from the integration of three key technologies (KTs): Rate-Splitting Multiple Access (RSMA), cell-free architectures, and federated learning. Each of these technologies offers distinct advantages in terms of security, robustness, and distributed structure. In this paper, we propose a novel cell-free network architecture that incorporates RSMA and employs machine learning techniques within a federated framework. This combination leverages the strengths of each KT, creating a synergistic effect that maximizes the benefits of security, robustness, and distributed structure. We formally formulate the access point (AP) selection and precoder design for max-min rate optimization in a cell-free MIMO RSMA network. Our proposed solution scheme involves a three-block procedure. The first block trains deep reinforcement learning (DRL) neural networks to obtain RSMA precoders, assuming full connectivity between APs and user equipments (UEs). The second block uses these precoders and principal component analysis (PCA) to assign APs to UEs by removing a subset of AP-UE connections. The final block fine-tunes the RSMA precoders by incorporating the associated APs into a second DRL network. To leverage the distributed nature of the cell-free network, this process is implemented in a Federated Deep Reinforcement Learning (FDRL) structure operating through the cooperation of APs and a central processing unit (CPU). Simulation results demonstrate that the proposed FDRL approach performs comparably to a benchmark centralized DRL scheme. Our FDRL approach, provides a balanced trade-off, maintaining high performance with enhanced security and reduced processing demands.