Performance Optimization on Cell-Free Massive MIMO-Aided URLLC Systems With User Grouping

Inter-user interference and pilot contamination are the major obstacles limiting the performance of cell-free massive multiple-input multiple-output (CF mMIMO)-aided ultra-reliable low-latency communication (URLLC) systems. In this paper, user grouping is utilized to address these issues, by allocating users to groups based on frequency band division, preventing interference among users within different groups, and eliminating pilot contamination between different groups. We consider an uplink CF mMIMO-aided URLLC system with user grouping and derive the lower bound for the ergodic rate. Due to the limited blocklength of each group necessitating pilot reuse, a weight sum rate (WSR) maximum problem is formulated by jointly optimizing user grouping, pilot assignment, and power control. We propose a user grouping scheme based on graph theory, where iteratively searching for specific negative loops in the weighted directed graph can approach the optimal user grouping matrix. As the user grouping matrix updates at each iteration, we update the pilot assignment matrix based on graph theory and employ logarithmic function approximation and fractional programming for power control updates. Numerous numerical results demonstrate the effectiveness of user grouping, and the proposed algorithm improves WSR by 25% compared to the non-grouping algorithm, while outperforming other benchmark algorithms.