Scalable Association of Users in CF-mMIMO: A Synergy of Communication, Sensing, and JCAS
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.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Optimal Joint Radar and Communication User Association in Cell-Free mMIMO Systems
The cell-free massive multiple-input multiple-output (CF-mMIMO) systems are crucial for 6G development due to their high spectral efficiency and uniform user-experienced data rates. A key aspect of CF-mMIMO is user assoc…
User Coordination for Fast Beam Training in FDD Multi-User Massive MIMO
Massive multiple-input multiple-output (mMIMO) communications are one of the enabling technologies of 5G and beyond networks. While prior work indicates that mMIMO networks employing time division duplexing have a signif…
Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networks
Federated learning (FL) is a distributed learning paradigm wherein users exchange FL models with a server instead of raw datasets, thereby preserving data privacy and reducing communication overhead. However, the increas…
Federated LearningSecure Power Control for Downlink Cell-Free Massive MIMO With Passive Eavesdroppers
This work studies secure communications for a cell-free massive multiple-input multiple-output (CF-mMIMO) network which is attacked by multiple passive eavesdroppers overhearing communications between access points (APs)…
DiversityHybrid Network- and User-Centric Scalable Cell-Free Massive MIMO for Fronthaul Signaling Minimization
Cell-free massive multiple-input multiple-output (CFmMIMO) coordinates a great number of distributed access points (APs) with central processing units (CPUs), effectively reducing interference and ensuring uniform servic…
CPU