User Activity Detection, Channel Estimation and Sensing Parameter Estimation in Cell-Free Massive MIMO Systems

As one of the key enabling technologies in 6G, cell-free massive multiple-input multiple-output (CFmMIMO) helps to achieve a macro-diversity via a large number of distributed access points, having its potential to improve communication and sensing performance significantly. In this paper, we investigate user activity detection, channel estimation, and sensing parameter estimation in CFmMIMO systems. First, we formulate a user activity detection and channel estimation problem as a multiple measurement vectors compressive sensing problem by leveraging the sparse nature of sporadic user traffic in CFmMIMO. A two-stage algorithm is proposed to solve user activity detection, channel estimation, and sensing parameter estimation problem: the first stage detects user activity and estimates effective channel coefficients; the second stage estimates the range and angle parameters associated with active users based on the estimated effective channel coefficients. Simulation results demonstrate that the proposed two-stage algorithm can provide accurate activity detection and sensing parameters estimation results.