User Grouping and Scheduling for Joint Spatial Division and Multiplexing in FDD Massive MIMO System

Gang Wang,Yuting Lu,Xiaohui Bi,Jianing Zhao,Fuyu Hou

doi:10.4236/ijcns.2017.108b019

Abstract

Massive multiple-input multiple-output (MIMO) is a key technology for the 5th Generation wireless communication systems. Joint spatial division and multiplexing (JSDM) method can reduce the cost of downlink training and feedback for CSIT of frequency-division duplexing (FDD) system. In this paper, we studied user grouping and user scheduling problems based on JSDM, and proposed an improved greedy user scheduling algorithm with lower complexity. Numerical results show that the user grouping and scheduling based on the proposed improved algorithm have much lower computational complexity while resulting in only a slight loss in system performance.

Highlights

Massive multiple-input multiple-output (MIMO) is one of the core technologies expected to be adopted by the generation of wireless communication systems
Joint spatial division and multiplexing (JSDM) method can reduce the cost of downlink training and feedback for channel state information at transmitter (CSIT) of frequency-division duplexing (FDD) system
We studied user grouping and user scheduling problems based on JSDM, and proposed an improved greedy user scheduling algorithm with lower complexity

Summary

Introduction

Massive multiple-input multiple-output (MIMO) is one of the core technologies expected to be adopted by the generation of wireless communication systems. It is important to investigate the massive MIMO design for FDD systems. Joint spatial division and multiplexing (JSDM) was proposed to reduce the cost of FDD massive MIMO [5]. The idea of JSDM is to partition the user population into groups with similar channel covariance eigenspace, and split the downlink precoding into two stages. The pre-beamforming matrix is used to suppress the interference between different groups, and the MU-MIMO precoding matrix is designed to reduce the multiuser interference within each group. We research two different similarity measure methods and two clustering methods [6]. After user grouping, another important issue is user scheduling. The efficacy of the proposed schemes is validated with theoretical analysis and simulations

System Model

User Grouping in Massive Mimo System

Pre-Beamforming and Precoding Matrix

User Scheduling in Massive Mimo Systems

Simulation Study

Conclusion