Investigations into the Effect of Spatial Correlation on Channel Estimation and Capacity of Multiple Input Multiple Output System

Xia Liu,Feng Wang,Marek E Bialkowski

doi:10.4236/ijcns.2009.24029

Abstract

The paper reports on investigations into the effect of spatial correlation on channel estimation and capacity of a multiple input multiple output (MIMO) wireless communication system. Least square (LS), scaled least square (SLS) and minimum mean square error (MMSE) methods are considered for estimating channel properties of a MIMO system using training sequences. The undertaken mathematical analysis reveals that the accuracy of the scaled least square (SLS) and minimum mean square error (MMSE) channel estimation methods are determined by the sum of eigenvalues of the channel correlation matrix. It is shown that for a fixed transmitted power to noise ratio (TPNR) assumed in the training mode, a higher spatial correlation has a positive effect on the performance of SLS and MMSE estimation methods. The effect of accuracy of the estimated Channel State Information (CSI) on MIMO system capacity is illustrated by computer simulations for an uplink case in which only the mobile station (MS) transmitter is surrounded by scattering objects.

Highlights

In recent years, there has been a growing interest in multiple input multiple output (MIMO) techniques in relation to wireless communication systems as they can significantly increase data throughput without the need for extra operational frequency bandwidth
The undertaken mathematical analysis reveals that the accuracy of the scaled least square (SLS) and minimum mean square error (MMSE) channel estimation methods are determined by the sum of eigenvalues of the channel correlation matrix
The effect of accuracy of the estimated Channel State Information (CSI) on MIMO system capacity is illustrated by computer simulations for an uplink case in which only the mobile station (MS) transmitter is surrounded by scattering objects

Summary

Introduction

There has been a growing interest in multiple input multiple output (MIMO) techniques in relation to wireless communication systems as they can significantly increase data throughput (capacity) without the need for extra operational frequency bandwidth. It has been shown that the accuracy of the investigated training-based estimation methods is influenced by the transmitted power to noise ratio (TPNR) in the training mode, and a number of antenna elements at the transmitter and receiver. It has been pointed out that when TPNR and a number of antenna elements are fixed, the SLS and MMSE methods offer better performance than the LS method. This is due to the fact that SLS and MMSE methods utilize the channel correlation in the estimator cost function while the LS estimator does not take the channel properties into account

Methods

Results

Conclusion