AN IMPROVED MARKOV CHAIN MONTE CARLO METHOD FOR MIMO ITERATIVE DETECTION AND DECODING-中国期刊网

摘要 Recently,anewsoft-insoft-outdetectionalgorithmbasedontheMarkovChainMonteCarlo(MCMC)simulationtechniqueforMultiple-InputMultiple-Output(MIMO)systemsisproposed,whichisshowntoperformsignificantlybetterthantheirspheredecodingcounterpartswithrelativelylowcomplexity.However,theMCMCsimulatorislikelytogettrappedinafixedstatewhenthechannelSNRishigh,thuslotsofrepetitivesamplesareobservedandtheaccuracyofAPosterioriProbability(APP)estimationdeteriorates.Tosolvethisproblem,animprovedversionofMCMCsimulator,namedforced-dispersedMCMCalgorithmisproposed.Basedontheaposteriorivarianceofeachbit,theGibbssamplerismonitored.Oncethetrappedstateisdetected,thesampleisdispersedintentionallyaccordingtotheaposteriorivariance.Extensivesimulationshowsthat,comparedwiththeexistingsolution,theproposedalgorithmenablesthemarkovchaintotravelmorestates,whichensuresanear-optimalperformance.

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AN IMPROVED MARKOV CHAIN MONTE CARLO METHOD FOR MIMO ITERATIVE DETECTION AND DECODING

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AN IMPROVED MARKOV CHAIN MONTE CARLO METHOD FOR MIMO ITERATIVE DETECTION AND DECODING

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