Distributed Graph Neural Network Design for Sum Ergodic Spectral Efficiency Maximization in Cell-Free Massive MIMO

TL;DR

This paper introduces a distributed graph neural network framework for power allocation in cell-free massive MIMO systems, achieving near-centralized performance with reduced computational burden.

Contribution

It presents a novel distributed GNN-based method for resource allocation in cell-free MIMO, avoiding centralized data collection and reducing complexity.

Findings

Achieves sum ergodic rate close to centralized schemes

Outperforms traditional model-based optimization

Reduces computational complexity and data exchange

Abstract

This paper proposes a distributed learning-based framework to tackle the sum ergodic rate maximization problem in cell-free massive multiple-input multiple-output (MIMO) systems by utilizing the graph neural network (GNN). Different from centralized schemes, which gather all the channel state information (CSI) at the central processing unit (CPU) for calculating the resource allocation, the local resource of access points (APs) is exploited in the proposed distributed GNN-based framework to allocate transmit powers. Specifically, APs can use a unique GNN model to allocate their power based on the local CSI. The GNN model is trained at the CPU using the local CSI of one AP, with partially exchanged information from other APs to calculate the loss function to reflect system characteristics, capturing comprehensive network information while avoiding computation burden. Numerical results show that the proposed distributed learning-based approach achieves a sum ergodic rate close to that of centralized learning while outperforming the model-based optimization.