Doubly-Selective Channel Estimation Using Superimposed Training and Discrete Prolate Spheroidal Basis Models
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.
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