Fast channel estimation using maximum-length shift-register sequences

Authors:
Songnan Xi;Hsiao-Chun Wu;Tho Le-Ngoc;Arjan Durresi
Affiliations:
Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.;Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.;Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, H3A 2A7, Canada.;Department of Computer Science and Information Science, Purdue School of Science, IUPUI, Indianapolis, IN 46202, USA
Venue:
International Journal of Wireless and Mobile Computing
Year:
2010

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Abstract

Least-square channel estimation techniques usually involve the large-dimensional matrix inversion, whose heavy computational complexity cannot be extendable for long channel filters. Maximum-Length Shift-Register (MLSR) sequences, or m-sequences, possess the well controlled second order cyclic statistics and have been used as the training sequences for least-square channel estimators. In this paper, we analyse the statistical characteristics of m-sequences and design a corresponding highly computationally-efficient channel estimation algorithm. Two crucial measures, namely mean-square error and computational complexity, are evaluated thereupon. It can be justified that our proposed algorithm can achieve both efficiency and satisfactory performance for communication channel estimation.