Doubly selective channel estimation using superimposed training and exponential bases models

Authors:
Jitendra K. Tugnait;Xiaohong Meng;Shuangchi He
Affiliations:
Department of Electrical and Computer Engineering, Auburn University, Auburn, AL;Department of Electrical and Computer Engineering, Auburn University, Auburn, AL and Department of Design Verification, MIPS Technologies Inc., Mountain View, CA;Department of Electrical and Computer Engineering, Auburn University, Auburn, AL
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2006

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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.