Pilot Contamination-Aware Graph Attention Network for Power Control in CFmMIMO

TL;DR

This paper introduces a self-supervised graph attention network for power control in CFmMIMO systems that effectively manages pilot contamination and adapts to varying numbers of users, offering a practical alternative to traditional iterative algorithms.

Contribution

It proposes a novel GAT-based method that handles pilot contamination and dynamic user numbers without supervised training, improving real-time applicability.

Findings

Outperforms traditional methods in power control accuracy.

Effectively manages pilot contamination issues.

Adapts to varying user numbers in CFmMIMO systems.

Abstract

Optimization-based power control algorithms are predominantly iterative with high computational complexity, making them impractical for real-time applications in cell-free massive multiple-input multiple-output (CFmMIMO) systems. Learning-based methods have emerged as a promising alternative, and among them, graph neural networks (GNNs) have demonstrated their excellent performance in solving power control problems. However, all existing GNN-based approaches assume ideal orthogonality among pilot sequences for user equipments (UEs), which is unrealistic given that the number of UEs exceeds the available orthogonal pilot sequences in CFmMIMO schemes. Moreover, most learning-based methods assume a fixed number of UEs, whereas the number of active UEs varies over time in practice. Additionally, supervised training necessitates costly computational resources for computing the target power control solutions for a large volume of training samples. To address these issues, we propose a graph attention network for downlink power control in CFmMIMO systems that operates in a self-supervised manner while effectively handling pilot contamination and adapting to a dynamic number of UEs. Experimental results show its effectiveness, even in comparison to the optimal accelerated projected gradient method as a baseline.