%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 }