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...

• Type: Journal
• Year: 2008
• Where: TSP
• Authors: Shuangchi He, Jitendra K. Tugnait
• Keywords: Channel Estimation, Information Sequence, Superimposed Training, TSP 2008