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Doubly Selective Channel Estimation Using Superimposed Training and Exponential Bases Models

  • Jitendra K TugnaitEmail author
  • Xiaohong Meng
  • Shuangchi He
Open Access
Research Article
Part of the following topical collections:
  1. Reliable Communications over Rapidly Time-Varying Channels

Abstract

Channel estimation for single-input multiple-output (SIMO) frequency-selective 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 (nonrandom) 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 CE-BEM and 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, based on CE-BEM. Three illustrative computer simulation examples are presented including two where a frequency-selective channel is randomly generated with different Doppler spreads via Jakes' model.

Keywords

Quantum Information Information Sequence Channel Estimation Training Sequence Doppler Spread 

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Copyright information

© Tugnait et al. 2006

Authors and Affiliations

  • Jitendra K Tugnait
    • 1
    Email author
  • Xiaohong Meng
    • 1
    • 2
  • Shuangchi He
    • 1
  1. 1.Department of Electrical and Computer EngineeringAuburn UniversityAuburnUSA
  2. 2.Department of Design VerificationMIPS Technologies Inc.Mountain ViewUSA

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