A Random Access Protocol for Crowded Massive MIMO Systems Based on a Bayesian Classifier

Abstract

The strongest user collision resolution (SUCRe) protocol is an efficient grant-based random access (RA) solution to provide connectivity for large numbers of user equipments (UEs), leveraging massive MIMO propagation features, typically available in fifth-generation and beyond networks. In this letter, we propose to replace the retransmission rule of SUCRe protocol by a Bayesian classifier for identifying the strongest user, aiming to resolve the collisions in a decentralized way, at the UEs’ side. As an offline training stage, we first conduct a statistical learning procedure to obtain the density estimations of the UEs’ decision variables in the SUCRe protocol, both in the cases of the UE being the strongest contender or not. Then, following the maximum a posteriori decision criterion, we determine how the UE can decide if it is the strongest contender (retransmitting the chosen RA pilot) or not (staying idle and trying again later). The numerical results show that our proposed method achieves significant connectivity performance improvements compared with other protocols, without requiring any additional complexity or overhead.