Kalman filtering: theory and practice
Kalman filtering: theory and practice
Introduction to statistical signal processing with applications
Introduction to statistical signal processing with applications
Frequency-shift zero-forcing time-varying equalization for doubly selective SIMO channels
EURASIP Journal on Applied Signal Processing
Estimation and direct equalization of doubly selective channels
EURASIP Journal on Applied Signal Processing
Time-Variant Channel Estimation Using Discrete Prolate Spheroidal Sequences
IEEE Transactions on Signal Processing
Linear prediction error method for blind identification of periodically time-varying channels
IEEE Transactions on Signal Processing
Time-varying FIR equalization for doubly selective channels
IEEE Transactions on Wireless Communications
Low complexity equalization for doubly selective channels modeled by a basis expansion
IEEE Transactions on Signal Processing
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In this paper, we exploit the Kalman filter as a time-varying linear minimum mean-square error equalizer for doubly-selective fading channels. We use a basis expansion model (BEM) to approximate the doubly-selective channel impulse response. Several time-varying linear equalizers have been proposed in the literature where both the channel and the equalizer impulse responses are approximated by complex exponential (CE) BEMs. Our proposed Kalman filter formulation does not rely on a specific BEM for the underlying channel, therefore, it can be applied to any BEM, including the CE-BEM and the discrete prolate spheroidal (DPS) BEM. Moreover, the Kalman filter relies solely on the channel model and therefore, does not incur any approximation error inherent in the CE-BEM representation of the equalizer. Through computer simulations, we show that compared to two of the existing algorithms, the proposed Kalman filter formulation yields the same or an improved bit error rate at a much lower computational cost, where the latter is measured in terms of the number of flops needed for the equalizer design and implementation.