Paper Info
Title
Markov chain monte carlo detectors for channels with intersymbol interference
Abstract
In this paper, we propose novel low-complexity soft-in soft-out (SISO) equalizers using the Markov chain Monte Carlo (MCMC) technique. We develop a bitwise MCMC equalizer (b-MCMC) that adopts a Gibbs sampler to update one bit at a time, as well as a group-wise MCMC (g-MCMC) equalizer where multiple symbols are updated simultaneously. The g-MCMC equalizer is shown to outperform both the b-MCMC and the linear minimum mean square error (MMSE) equalizer significantly for channels with severe amplitude distortion. Direct application of MCMC to channel equalization requires sequential processing which leads to long processing delay. We develop a parallel processing algorithm that reduces the processing delay by orders of magnitude. Numerical results show that both the sequential and parallel processing MCMC equalizers perform similarly well and achieve a performance that is only slightly worse than the optimum maximum a posteriori (MAP) equalizer. The MAP 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 MCMC equalizers grows linearly.
Year
DOI
Venue
2010
10.1109/TSP.2009.2038958
IEEE Transactions on Signal Processing
Keywords
Field
DocType
helium,channel capacity,gibbs sampler,intersymbol interference,markov processes,channel equalization,parallel processing,markov chain monte carlo,equalization,monte carlo methods,decoding,detectors
Intersymbol interference,Monte Carlo method,Equalization (audio),Markov chain Monte Carlo,Control theory,Computer science,Algorithm,Minimum mean square error,Adaptive equalizer,Speech recognition,Maximum a posteriori estimation,Processing delay
Journal
Volume
Issue
ISSN
58
4
1053-587X
Citations 
PageRank 
References 
18
0.97
18
Authors
3
Name
Order
Citations
PageRank
Ronghui Peng1614.93
Rong-Rong Chen27010.31
Behrouz Farhang-Boroujeny397284.30