Introduction to statistical signal processing with applications
Introduction to statistical signal processing with applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Matrix computations (3rd ed.)
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Estimation and direct equalization of doubly selective channels
EURASIP Journal on Applied Signal Processing
Multi-input multi-output fading channel tracking and equalizationusing Kalman estimation
IEEE Transactions on Signal Processing
Optimal training for block transmissions over doubly selective wireless fading channels
IEEE Transactions on Signal Processing
Time-Variant Channel Estimation Using Discrete Prolate Spheroidal Sequences
IEEE Transactions on Signal Processing
Improved bayesian MIMO channel tracking for wireless communications: incorporating a dynamical model
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
MMSE decision-feedback equalizers: finite-length results
IEEE Transactions on Information Theory
Receiver structures for time-varying frequency-selective fading channels
IEEE Journal on Selected Areas in Communications
Time-varying FIR equalization for MIMO transmission over doubly selective channels
EURASIP Journal on Advances in Signal Processing - Special issue on advanced equalization techniques for wireless communications
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Three versions of a novel adaptive channel estimation approach, exploiting the over-sampled complex exponential basis expansion model (CE-BEM), is presented for doubly selective channels, where we track the BEM coefficients rather than the channel tap gains. Since the time-varying nature of the channel is well captured in theCE-BEM by the known exponential basis functions, the time variations of the (unknown) BEM coefficients are likely much slower than those of the channel, and thus more convenient to track.We propose a "subblockwise" tracking scheme for the BEM coefficients using time-multiplexed (TM) periodically transmitted training symbols. Three adaptive algorithms, including a Kalman filtering scheme based on an assumed autoregressive (AR) model of the BEM coefficients, and two recursive least-squares (RLS) schemes not requiring any model for the BEM coefficients, are investigated for BEM coefficient tracking. Simulation examples illustrate the superior performance of our approach over several existing doubly selective channel estimators.