Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks

TL;DR

This paper introduces a lightweight transformer with linear attention for joint user-centric clustering and power optimization in cell-free networks, improving scalability, flexibility, and efficiency over existing methods.

Contribution

It proposes a novel architecture-agnostic transformer model that jointly predicts AP clusters and powers from spatial data, addressing pilot contamination and computational complexity issues.

Findings

Achieves near-optimal spectral efficiency in simulations.

Handles dynamic network configurations without channel estimation.

Scales linearly with the number of users.

Abstract

Optimal AP clustering and power allocation are critical in user-centric cell-free massive MIMO systems. Existing deep learning models lack flexibility to handle dynamic network configurations. Furthermore, many approaches overlook pilot contamination and suffer from high computational complexity. In this paper, we propose a lightweight transformer model that overcomes these limitations by jointly predicting AP clusters and powers solely from spatial coordinates of user devices and AP. Our model is architecture-agnostic to users load, handles both clustering and power allocation without channel estimation overhead, and eliminates pilot contamination by assigning users to AP within a pilot reuse constraint. We also incorporate a customized linear attention mechanism to capture user-AP interactions efficiently and enable linear scalability with respect to the number of users. Numerical results confirm the model's effectiveness in maximizing the minimum spectral efficiency and providing near-optimal performance while ensuring adaptability and scalability in dynamic scenarios.