| Paper: | SS-3.1 | ||
| Session: | Wireless Communications over Rapidly Time-Varying Channels | ||
| Time: | Tuesday, May 18, 15:30 - 15:47 | ||
| Presentation: | Special Session Lecture | ||
| Topic: | Special Sessions: Wireless Communications over Rapidly Time-Varying Channels | ||
| Title: | SEMI-BLIND TIME-VARYING CHANNEL ESTIMATION USING SUPERIMPOSED TRAINING | ||
| Authors: | Xiaohong Meng; Auburn University | ||
| Jitendra Tugnait; Auburn University | |||
| Abstract: | Channel estimation for single-input multiple-output (SIMO) 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 (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 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. An illustrative computer simulation example is presented where a frequency-selective channel is randomly generated with different Doppler spreads via Jakes' mod | ||
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