Thesis
Iterative row-column algorithms for two-dimensional intersymbol interference channel equalization: complexity reduction and performance enhancement
Washington State University
Master of Science (MS), Washington State University
2010
Handle:
https://hdl.handle.net/2376/100681
Abstract
This thesis uses the iterative row-column soft-decision feedback algorithm (IRCSDFA) of Cheng et.al. IEEE Sig. Proc. Letters 2007 as a starting point; comparisons in that work show that the IRCSDFA is one of the leading algorithms for turbo equalization of two dimensional inter symbol interference (ISI) channels with additive white Gaussian noise. In this thesis we first propose a feedback probability sorting algorithm with adaptive thresholding that reduces the computational complexity of the IRCSDFA by over 96%, with very little performance degradation. A similar but simpler non-adaptive sorting scheme was proposed for 4-ary 2D ISI channels in Zhu et.al. CISS2009, but its performance degradation could not be fully assessed due to the very high computational complexity of the full 4-ary IRCSDFA. This thesis we then propose a Squared Euclidean Distance (SED) based local search post-processing scheme that operates on the final Log-likelihood Ratio (LLR) estimates output by the IRCSDFA. The basic and advanced schemes exploit the spatial correlation of the low reliability LLRs to find clusters, and then perform a local SED search on each cluster to find the estimation. Experiments show that the SED search yields performance improvements of up to 0.4 dB with less than 12.5% increase in computational complexity. As error clustering is present in many 2D ISI equalization algorithms, it is likely that the proposed SED search scheme could improve the performance of other 2D ISI algorithms as well.
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Details
- Title
- Iterative row-column algorithms for two-dimensional intersymbol interference channel equalization
- Creators
- Hannan Ma
- Contributors
- Benjamin Joseph Belzer (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; Pullman, Wash. :
- Identifiers
- 99900525116301842
- Language
- English
- Resource Type
- Thesis