Appropriate Algorithms for Estimating Frequency-Selective Rician Fading MIMO Channels and Channel Rice Factor: Substantial Benefits of Rician Model and Estimator Tradeoffs

Hamid Nooralizadeh,Shahriar Shirvani Moghaddam

doi:10.1155/2010/753637

Abstract

The training-based channel estimation (TBCE) scheme in multiple-input multiple-output (MIMO) frequency-selective Rician fading channels is investigated. We propose the new technique of shifted scaled least squares (SSLS) and the minimum mean square error (MMSE) estimator that are suitable to estimate the above-mentioned channel model. Analytical results show that the proposed estimators achieve much better minimum possible Bayesian Cramér-Rao lower bounds (CRLBs) in the frequency-selective Rician MIMO channels compared with those of Rayleigh one. It is seen that the SSLS channel estimator requires less knowledge about the channel and/or has better performance than the conventional least squares (LS) and MMSE estimators. Simulation results confirm the superiority of the proposed channel estimators. Finally, to estimate the channel Rice factor, an algorithm is proposed, and its efficiency is verified using the result in the SSLS and MMSE channel estimators.

Highlights

In wireless communications, multiple-input multipleoutput (MIMO) systems provide substantial benefits in both increasing system capacity and improving its immunity to deep fading in the channel [1, 2]
In outdoor MIMO channels where channels are under fast fading, the channel tracking and estimating algorithms as the Wiener least mean squares (W-LMS) [13], the Kalman filter [14], recursive least squares (RLS) [15], generalized RLS (GRLS) [16], and generalized LMS (GLMS) [17] are used
The performance of the LS, scaled LS (SLS), shifted scaled least squares (SSLS), and minimum mean square error (MMSE) channel estimators is numerically examined in the frequency-selective Rayleigh and Rician fading channels

Summary

Introduction

Multiple-input multipleoutput (MIMO) systems provide substantial benefits in both increasing system capacity and improving its immunity to deep fading in the channel [1, 2]. In most previous research on the coding approaches for MIMO systems, the accurate channel state information (CSI) is required at the receiver and/or transmitter. Three classes of methods for channel identification are presented. They include training-based channel estimation (TBCE) [7, 8], blind channel estimation (BCE) [9, 10], and semiblind channel estimation (SBCE) [11, 12]. In outdoor MIMO channels where channels are under fast fading, the channel tracking and estimating algorithms as the Wiener least mean squares (W-LMS) [13], the Kalman filter [14], recursive least squares (RLS) [15], generalized RLS (GRLS) [16], and generalized LMS (GLMS) [17] are used

Objectives

Results

Conclusion