NDLI: SPC02-3: Doubly-Selective Channel Estimation Using Superimposed Training and Discrete Prolate Spheroidal Basis Models

Content Provider	IEEE Xplore Digital Library
Author	Shuangchi He Tugnait, J.K.
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Auburn Univ., Auburn, AL (Shuangchi He; Tugnait, J.K.)
Abstract	Channel estimation for single user frequency- selective time-varying channel is considered using superimposed training. The time-varying channel is assumed to be well- described by a basis expansion model using discrete prolate spheroidal sequences (DPS-BEM). A periodic (non-random) 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 DPS-BEM and only the first-order statistics of the observations. 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 channel and the information sequences sequentially, based on DPS-BEM. Illustrative computer simulation examples are presented where a frequency-selective channel is randomly generated with different Doppler spreads via Jakes' model. Simulations show that the proposed approaches are competitive with time-multiplexed training without incurring data-rate loss.
Starting Page	1
Ending Page	6
File Size	217205
Page Count	6
File Format	PDF
ISBN	1424403561
ISSN	1930529X
DOI	10.1109/GLOCOM.2006.540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Channel estimation Time-varying channels Frequency estimation Transmitters Statistics Viterbi algorithm Detectors Maximum likelihood detection Maximum likelihood estimation Computer simulation
Content Type	Text
Resource Type	Article

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