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2008

On Doubly Selective Channel Estimation Using Superimposed Training and Discrete Prolate Spheroidal Sequences

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On Doubly Selective Channel Estimation Using Superimposed Training and Discrete Prolate Spheroidal Sequences
Abstract--Channel estimation and data detection for frequencyselective time-varying channels are considered using superimposed training. We employ a discrete prolate spheroidal basis expansion model (DPS-BEM) to describe the time-varying channel. A periodic (nonrandom) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission; therefore, there is no loss in data transmission rate compared to time-multiplexed (TM) training. We first estimate the channel using DPS-BEM and only the first-order statistics of the observations. In this estimator the unknown information sequence acts as interference resulting in a poor signal-to-noise-and-interference ratio (SNIR) for channel estimation. We then apply a data-dependent superimposed training sequence, to either totally or partially cancel out the effects of the unknown information sequence at the receiver on channel estimation. In total cancellation, a...
Shuangchi He, Jitendra K. Tugnait
Added 28 Jan 2011
Updated 28 Jan 2011
Type Journal
Year 2008
Where TSP
Authors Shuangchi He, Jitendra K. Tugnait
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