Scalable Association of Users in CF-mMIMO: A Synergy of Communication, Sensing, and JCAS

TL;DR

This paper introduces a scalable user association scheme for CF-mMIMO networks that balances communication, sensing, and JCAS needs, improving performance and reducing interference compared to traditional methods.

Contribution

It proposes a novel, dynamic user association method that considers heterogeneous UE requirements and integrates communication, sensing, and JCAS functionalities in CF-mMIMO systems.

Findings

Reduces interference and computational runtime.

Improves symbol error rate for communication.

Enhances probability of detection for sensing.

Abstract

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a key enabler for the sixth generation (6G) networks, offering unprecedented spectral efficiency and ubiquitous coverage. In CF-mMIMO systems, the association of user equipments (UEs) to access points (APs) is a critical challenge, as it directly impacts network scalability, interference management, and overall system performance. Conventional association methods primarily focus on optimizing communication performance. However, with the emergence of sensing and joint communication and sensing (JCAS) requirements, conventional approaches become insufficient. To address this challenge, we propose a scalable user association (SUA) scheme for CF-mMIMO networks, considering heterogeneous UE requirements. Designed to enhance the performance of both sensing and communication, the proposed SUA scheme aims to ensure network scalability. This is achieved by dynamically assigning APs to UEs based on their specific service requirements (communication, sensing, or JCAS), while considering link quality, interference mitigation, and network-related constraints. Specifically, the proposed SUA scheme employs AP masking, link prioritization, and an optimization-based association mechanism to select the most suitable APs for each UE. Simulations show that, compared to conventional CF-mMIMO methods, the proposed SUA scheme significantly reduces interference and computational runtime, while improving the symbol error rate for communication and the probability of detection for sensing.