Cluster-Based 3-D Channel Modeling for Massive MIMO in Subway Station Environment

Abstract

In this paper, a massive multiple-input multiple-output (MIMO) channel measurement campaign with 256-element virtual rectangular array at the base station was conducted. The typical hotspot scenario, subway station, is considered, and the measurements were conducted at 6 GHz with a bandwidth of 100 MHz. A hybrid clustering approach is proposed to characterize the cluster evolution over the large-scale array. In the hybrid approach, we apply the space-alternating generalized expectation maximization algorithm to estimate the multipath components (MPCs), and use the multipath component distance-based tracking algorithm and the KPowerMeans algorithm for MPCs tracking and clustering. A cluster partition algorithm is further proposed to adjudge the clusters association over the array, and output the final clustering results. Under such a scheme, cluster-based model parameters are provided with detailed analysis. The extracted parameters include overall angle distribution, global angular spread, inter-cluster parameters, and intra-cluster parameters. The obtained model parameters can be fed into the new channel simulator for massive MIMO. This is useful for the design and application of the practical massive MIMO system in the future.