EURASIP Journal on Applied Signal Processing 
Volume 2006 (2006), Article ID 85303, 11 pages
doi:10.1155/ASP/2006/85303

Doubly Selective Channel Estimation Using Superimposed Training and Exponential Bases Models

Jitendra K. Tugnait,1 Xiaohong Meng,1,2 and Shuangchi He1

 1Department of Electrical and Computer Engineering, Auburn University, Auburn 36849, AL, USA
2Department of Design Verification, MIPS Technologies Inc., Mountain View 94043, CA, USA
Received 1 June 2005; Revised 2 June 2006; Accepted 4 June 2006

Abstract

Channel estimation for single-input multiple-output (SIMO) frequency-selective time-varying channels is considered using superimposed training. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). A periodic (nonrandom) training sequence is arithmetically added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. A two-step approach is adopted where in the first step we estimate the channel using CE-BEM and only the first-order statistics of the data. Using the estimated channel from the first step, a Viterbi detector is used to estimate the information sequence. In the second step, a deterministic maximum-likelihood (DML) approach is used to iteratively estimate the SIMO channel and the information sequences sequentially, based on CE-BEM. Three illustrative computer simulation examples are presented including two where a frequency-selective channel is randomly generated with different Doppler spreads via Jakes' model.