In this paper, we propose a novel low complexity soft-in soft-out (SISO) equalizer using the Markov chain Monte Carlo (MCMC) technique. Direct application of MCMC to SISO equalization (reported in a previous work) results in a sequential processing algorithm that leads to a long processing delay in the communication link. Using the tool of factor graph, we propose a novel parallel processing algorithm that reduces the processing delay by orders of magnitude. Numerical results show that, both the sequential and parallel processing SISO equalizers perform similarly well and achieve a performance that is only slightly worse than the optimum SISO equalizer. The optimum SISO equalizer, on the other hand, has a complexity that grows exponentially with the size of the memory of the channel, while the complexity of the proposed SISO equalizers grows linearly.
%0 Conference Paper
%1 5199153
%A Peng, Rong-Hui
%A Chen, Rong-Rong
%A Farhang-Boroujeny, B.
%B Communications, 2009. ICC '09. IEEE International Conference on
%D 2009
%K ISI MCMC complexity equalizer low-complexity
%P 1-5
%R 10.1109/ICC.2009.5199153
%T Low Complexity Markov Chain Monte Carlo Detector for Channels with Intersymbol Interference
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5199153
%X In this paper, we propose a novel low complexity soft-in soft-out (SISO) equalizer using the Markov chain Monte Carlo (MCMC) technique. Direct application of MCMC to SISO equalization (reported in a previous work) results in a sequential processing algorithm that leads to a long processing delay in the communication link. Using the tool of factor graph, we propose a novel parallel processing algorithm that reduces the processing delay by orders of magnitude. Numerical results show that, both the sequential and parallel processing SISO equalizers perform similarly well and achieve a performance that is only slightly worse than the optimum SISO equalizer. The optimum SISO equalizer, on the other hand, has a complexity that grows exponentially with the size of the memory of the channel, while the complexity of the proposed SISO equalizers grows linearly.
@inproceedings{5199153,
abstract = {In this paper, we propose a novel low complexity soft-in soft-out (SISO) equalizer using the Markov chain Monte Carlo (MCMC) technique. Direct application of MCMC to SISO equalization (reported in a previous work) results in a sequential processing algorithm that leads to a long processing delay in the communication link. Using the tool of factor graph, we propose a novel parallel processing algorithm that reduces the processing delay by orders of magnitude. Numerical results show that, both the sequential and parallel processing SISO equalizers perform similarly well and achieve a performance that is only slightly worse than the optimum SISO equalizer. The optimum SISO equalizer, on the other hand, has a complexity that grows exponentially with the size of the memory of the channel, while the complexity of the proposed SISO equalizers grows linearly.},
added-at = {2015-11-12T00:59:29.000+0100},
author = {Peng, Rong-Hui and Chen, Rong-Rong and Farhang-Boroujeny, B.},
biburl = {https://www.bibsonomy.org/bibtex/221599a79e70f8378d9c09efbe0a14974/krsch},
booktitle = {Communications, 2009. ICC '09. IEEE International Conference on},
doi = {10.1109/ICC.2009.5199153},
interhash = {5f0ec6ae687872376897b2467f3a4c14},
intrahash = {21599a79e70f8378d9c09efbe0a14974},
issn = {1938-1883},
keywords = {ISI MCMC complexity equalizer low-complexity},
month = {June},
pages = {1-5},
timestamp = {2015-11-12T00:59:29.000+0100},
title = {Low Complexity Markov Chain Monte Carlo Detector for Channels with Intersymbol Interference},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5199153},
year = 2009
}