Covariance-Based Activity Detection Algorithm in Clustered Cell-Free Massive MIMO

Massive Machine-Type Communications (mMTC) plays an increasingly important role in future wireless communications, and the use of a large number of low-power devices in conjunction with cell-free massive MIMO networks is a promising enabling technology. In this research, we explore activity detection for Grant-Free Random Access in massive mMTC within a cell-free Massive MIMO network employing distributed antenna arrays. Each active device sends a non-orthogonal pilot sequence to geographically clustered Access Points (APs) using the K-means algorithm. The clustered APs then relay the received signals to the Central Processing Unit (CPU) for collaborative activity detection. Simulation results demonstrate that for extensive coverage areas, the covariance-based activity detection algorithm with K-means clustered APs exhibits reduced complexity and improved activity detection efficiency while maintaining robust performance.