Robust User Scheduling with COST 2100 Channel Model for Massive MIMO Networks

TL;DR

This paper introduces a geometry-based user scheduling algorithm for Massive MIMO systems using the COST 2100 channel model, reducing channel estimation overhead while maintaining high sum-rate performance.

Contribution

A novel user selection method leveraging cluster geometry and location, with robustness analysis against cluster position errors in Massive MIMO networks.

Findings

The proposed GUS algorithm achieves sum-rate close to the GWC scheme.

Capacity depends significantly on cluster positions in the GSCMs.

The algorithm is robust to cluster localization errors.

Abstract

This paper considers a Massive multiple-input multiple-output (MIMO) network, where the base station (BS) with a large number of antennas communicates with a smaller number of users. The signals are transmitted using frequency division duplex (FDD) mode. The problem of user scheduling with reduced overhead of channel estimation in the uplink of Massive MIMO systems has been investigated. We consider the COST 2100 channel model. In this paper, we first propose a new user selection algorithm based on knowledge of the geometry of the service area and of location of clusters, without having full channel state information (CSI) at the BS. We then show that the correlation in geometry-based stochastic channel models (GSCMs) arises from the common clusters in the area. In addition, exploiting the closed-form Cramer-Rao lower bounds (CRLB)s, the analysis for the robustness of the proposed scheme to cluster position errors is presented. It is shown by analysing the capacity upper-bound that the capacity strongly depends on the position of clusters in the GSCMs and users in the system. Simulation results show that although the BS receiver does not require the channel information of all users, by the proposed geometry-based user scheduling (GUS) algorithm the sum-rate of the system is only slightly less than the well-known greedy weight clique (GWC) scheme \cite{SUSGoldsmithGlobcom,ITC09_Userselection_GWC}. {Finally, the robustness of the proposed algorithm to cluster localization is verified by the simulation results.