An Enhanced MCMC Algorithm for MIMO Systems Based on Max-Log Updating
HU Jian-hao;ZHOU Jiang-yun;HE Shuai-ning;CHEN Jie-nan;National Key Lab of Science and Technology on Communications,University of Electronic Science and Technology of China;
In this paper, an enhanced Markov chain Monte Carlo(MCMC) algorithm based on max-log updating is proposed for multiple input multiple output(MIMO) system. The max-log updating can generate the list vectors to simply the complexity of the calculation of the extrinsic log-likelihood ratios(LLRs) efficiently. Meanwhile, it avoids calculating probability distribution per bit in conventional MCMC. However, the proposed MCMC detection suffers from the so called "stalling" problem, where the Markov chain may be trapped into local optimal state. Thus, we also propose three enhancement technologies: 1) biased processing, i.e., updating randomly in a given biased interval; 2) reinitialized processing, i.e., reinitialize the Markov chain under the sub-optimal states; 3) clipped processing, i.e., reprocessing the LLR with clipping. Simulation results show that the proposed algorithm can remedy the "stalling" problem efficiently with reduced complexity, and can achieve 2 d B performance gains with 10% less complexity than MMSE-PIC.