International Journal of Digital Multimedia Broadcasting 
Volume 2008 (2008), Article ID 535269, 9 pages
doi:10.1155/2008/535269
Research Article

Iterative Mean Removal Superimposed Training for SISO and MIMO Channel Estimation

O. Longoria-Gandara,1 R. Parra-Michel,1 M. Bazdresch,2 and A. G. Orozco-Lugo3

 1Department of Electrical Engineering, CINVESTAV-IPN, Apartado Postal 31-438, Plaza La Luna, Guadalajara, 44550 JAL, Mexico
2Department of Electronics, Systems and Computer Science, ITESO, Per. Sur M. Gomez Morin 8585, Tlaquepaque, 45604 JAL, Mexico
3Department of Electrical Engineering, CINVESTAV-IPN, Apartado Postal 14-740, 07000 Mexico, DF, Mexico
Received 2 April 2008; Revised 1 August 2008; Accepted 22 September 2008

Recommended by Fred Daneshgaran

Abstract

This contribution describes a novel iterative radio channel estimation algorithm based on superimposed training (ST) estimation technique. The proposed algorithm draws an analogy with the data dependent ST (DDST) algorithm, that is, extracts the cycling mean of the data, but in this case at the receiver's end. We first demonstrate that this mean removal ST (MRST) applied to estimate a single-input single-output (SISO) wideband channel results in similar bit error rate (BER) performance in comparison with other iterative techniques, but with less complexity. Subsequently, we jointly use the MRST and Alamouti coding to obtain an estimate of the multiple-input multiple-output (MIMO) narrowband radio channel. The impact of imperfect channel on the BER performance is evidenced by a comparison between the MRST method and the best iterative techniques found in the literature. The proposed algorithm shows a good tradeoff performance between complexity, channel estimation error, and noise immunity.