NDLI: Doubly-selective fading channel equalization: A comparison of the Kalman filter approach with the basis expansion model-based equalizers

Content Provider	IEEE Xplore Digital Library
Author	Liying Song Tugnait, J.K.
Copyright Year	2002
Abstract	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.
Starting Page	60
Ending Page	65
Page Count	6
File Size	252745
File Format	PDF
ISSN	15361276
Volume Number	8
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fading Equalizers Finite impulse response filter Approximation error Bit error rate Computational efficiency Computational complexity Nonlinear filters Delay Computer simulation Kalman filtering Doubly-selective channels basis expansion models channel equalization
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering Computer Science Applications

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