Clustering Based Pilot Allocation Algorithm for Mitigating Pilot Contamination in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) systems are expected to achieve substantial gains in frequency efficiency compared to conventional MIMO systems. However, these promising gains are limited by the pilot contamination in multicell scenario, which results from reusing the same set of pilot sequences in neighboring cells. This paper proposes a clustering based pilot allocation (CPA) algorithm to mitigate the effects of pilot contamination. Specifically, we first define the inter-cell interference (ICI) relationship between users, based on which the users are divided into high and low ICI groups. Then, the users in low ICI group reuse pilots randomly, while the users in high ICI group are clustered by affinity propagation (AP) clustering method. Finally, we perform different pilot allocation strategies according to the types of user clusters. Simulation results show that the proposed algorithm can significantly improve the pilot efficiency compared to the existing pilot allocation algorithms using grouping ideas.