C-Footprints: A Statistic-Based Clustering for Pilot Allocation in Cell-Free Massive MIMO

User-centric cell-free massive MIMO (CF-mMIMO) communications rely on accurately knowing the channel state information (CSI) to perform coherent signal processing. However, achieving pilot contamination-free channel estimation is challenging due to the limited length of the coherence blocks. In this work, we combine a novel user equipment (UE) clustering method termed C-footprints and a sequential heuristic named cell-free-oriented best first pilot assignment (CF-BFPA) to find pilot allocations that effectively reduce contamination. First, the proposed clustering method groups potentially contaminating UEs based on the degree of orthogonality among the matrices of channel statistics. Subsequently, the CF-BFPA algorithm performs a greedy intra-cluster pilot assignment that ensures low interference between the most contaminating UEs. The performance of the proposed solution (C-footprints+CF-BFPA) is compared with several state-of-the-art algorithms across diverse CF-mMIMO network settings.