Graph Based User Clustering for HAP Massive MIMO Systems With Two-stage Beamforming

Abstract

We propose a user clustering algorithm based on graph theory with two-stage beamforming for high-altitude platform (HAP) massive multiple-input multiple-output (MIMO) systems. First, we construct a conflict graph, where each vertex is the user and each edge is measured by the similarity of correlation matrix distance (CMD) between users. Then, in the aim of alleviating the self-cluster interference (SCI), a novel low-complexiy user clustering method is introduced, where the algorithm is Bron-Kerbosch to enumerate all the maximal cliques, and maximal clusters are obtained by the cluster formation algorithm. As shown in the numerical results, the performance of the proposed algorithm has a significant increase.