Joint User Activity Detection and Channel Estimation for Cell-Free Massive MIMO in Asynchronous mMTC

Cell-free massive multiple-input multiple-output (CFmMIMO) is one of the key enabling technologies for massive machine type communication (mMTC), where user activity detection and channel estimation face a significant challenge due to asynchronous transmissions caused by either propagation delays among access points (APs) or low-cost oscillators. This work aims to propose a joint user activity detection and channel estimate approach for asynchronous mMTC scenarios in CFmMIMO. We deal with user activity detection and channel estimation as a structural compressive sensing problem, which is related to asynchronous latency, active users, and channel coefficients, making use of sporadic nature of mMTC traffic. In particular, we implement an asynchronous aware simultaneous orthogonal matching pursuit (AA-SOMP) scheme at central processing unit. This approach is effective to retrieve channel coefficients and active user information of numerous APs, even with restricted pilot length and asynchronous delays. Simulation results show that the proposed approach performs better than the existing algorithms.