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Doubly Selective Channel Estimation Using Superimposed Training and Exponential Bases Models

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.

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Correspondence to Jitendra K Tugnait.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Tugnait, J.K., Meng, X. & He, S. Doubly Selective Channel Estimation Using Superimposed Training and Exponential Bases Models. EURASIP J. Adv. Signal Process. 2006, 085303 (2006). https://doi.org/10.1155/ASP/2006/85303

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