Equalization of sparse intersymbol-interference channels revisited

Authors:
Jan Mietzner;Sabah Badri-Hoeher;Ingmar Land;Peter A. Hoeher
Affiliations:
Information and Coding Theory Lab (ICT), Faculty of Engineering, University of Kiel, Kiel, Germany;Information and Coding Theory Lab (ICT), Faculty of Engineering, University of Kiel, Kiel, Germany;Department of Communication Technology, Digital Communications Division, Aalborg University, Aalborg East, Denmark;Information and Coding Theory Lab (ICT), Faculty of Engineering, University of Kiel, Kiel, Germany
Venue:
EURASIP Journal on Wireless Communications and Networking
Year:
2006

Citing 7
Cited 0

Adaptive filter theory (2nd ed.)

Adaptive filter theory (2nd ed.)
Wireless Communications: Principles and Practice

Wireless Communications: Principles and Practice
Reduced complexity equalization for data communication

Reduced complexity equalization for data communication
On prefilter computation for reduced-state equalization

IEEE Transactions on Wireless Communications
Efficient decision feedback equalization for sparse wireless channels

IEEE Transactions on Wireless Communications
Fast computation of a discrete-time whitened matched filter based on Kalman filtering

IEEE Transactions on Wireless Communications
Efficiently computed reduced-parameter input-aided MMSE equalizers for ML detection: a unified approach

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

Sparse intersymbol-interference (ISI) channels are encountered in a variety of communication systems, especially in high-data-rate systems. These channels have a large memory length, but only a small number of significant channel coefficients. In this paper, equalization of sparse ISI channels is revisited with focus on trellis-based techniques. Due to the large channel memory length, the complexity of maximum-likelihood sequence estimation by means of the Viterbi algorithm is normally prohibitive. In the first part of the paper, a unified framework based on factor graphs is presented for complexity reduction without loss of optimality. In this new context, two known reduced-complexity trellis-based techniques are recapitulated. In the second part of the paper a simple alternative approach is investigated to tackle general sparse ISI channels. It is shown that the use of a linear filter at the receiver renders the application of standard reduced-state trellis-based equalization techniques feasible without significant loss of optimality.