基于Max-Log更新的馬爾科夫鏈蒙特卡洛MIMO檢測增強(qiáng)算法
發(fā)布時(shí)間：2018-11-07 13:22
【摘要】：針對(duì)傳統(tǒng)的馬爾科夫鏈蒙特卡洛(MCMC)算法,提出了一種基于Max-Log更新的MCMC-MIMO檢測算法。該算法采用了基于Max-Log更新的采樣,可以有效產(chǎn)生收斂于后驗(yàn)概率(APP)分布的比特樣本列表集合,同時(shí)可避免計(jì)算傳統(tǒng)MCMC算法中的每比特概率分布。但是該檢測算法在高信噪比下,采樣過程會(huì)陷入鎖死到局部最優(yōu)態(tài)。在此基礎(chǔ)上,提出了3個(gè)增強(qiáng)技術(shù):1)抖動(dòng)處理,對(duì)給定置信區(qū)間內(nèi)的更新進(jìn)行抖動(dòng)處理;2)條件下重新初始化,對(duì)處在潛在鎖死態(tài)的采樣序列進(jìn)行重新初始化;3)修剪飽和處理,利用球形譯碼算法中的修剪飽和技術(shù)來處理MIMO檢測輸出的對(duì)數(shù)似然信息(LLR)。仿真結(jié)果顯示,基于Max-Log更新的MCMC增強(qiáng)算法能有效地解決陷入鎖死的問題,從而提高系統(tǒng)性能并降低系統(tǒng)的計(jì)算復(fù)雜度。在復(fù)雜度為MMSE-PIC檢測算法的90%的基礎(chǔ)上,性能提高了2 d B。
[Abstract]:Aiming at the traditional Markov chain Monte Carlo (MCMC) algorithm, a MCMC-MIMO detection algorithm based on Max-Log update is proposed. The algorithm adopts the sampling based on Max-Log update, which can effectively generate the sample list set converging to the posterior probabilistic (APP) distribution, while avoiding the computation of the per bit probability distribution in the traditional MCMC algorithm. However, under the high SNR, the sampling process will be locked to the local optimal state. On this basis, three enhancement techniques are proposed: 1) jitter processing, 2) reinitialization of the sample sequence in a potential locked state, 2) reinitialization of the update within a given confidence interval. 3) pruning saturation processing, using pruning saturation technique in spherical decoding algorithm to deal with logarithmic likelihood information (LLR). Of MIMO detection output. Simulation results show that the MCMC enhancement algorithm based on Max-Log update can effectively solve the problem of locking, thus improving the performance of the system and reducing the computational complexity of the system. The complexity of the algorithm is 90% of that of the MMSE-PIC detection algorithm, and the performance is improved by 2 dB.
【作者單位】： 電子科技大學(xué)通信抗干擾國家級(jí)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金(6150010678,61371104)
【分類號(hào)】：TN919.3
，
本文編號(hào)：2316497
[Abstract]:Aiming at the traditional Markov chain Monte Carlo (MCMC) algorithm, a MCMC-MIMO detection algorithm based on Max-Log update is proposed. The algorithm adopts the sampling based on Max-Log update, which can effectively generate the sample list set converging to the posterior probabilistic (APP) distribution, while avoiding the computation of the per bit probability distribution in the traditional MCMC algorithm. However, under the high SNR, the sampling process will be locked to the local optimal state. On this basis, three enhancement techniques are proposed: 1) jitter processing, 2) reinitialization of the sample sequence in a potential locked state, 2) reinitialization of the update within a given confidence interval. 3) pruning saturation processing, using pruning saturation technique in spherical decoding algorithm to deal with logarithmic likelihood information (LLR). Of MIMO detection output. Simulation results show that the MCMC enhancement algorithm based on Max-Log update can effectively solve the problem of locking, thus improving the performance of the system and reducing the computational complexity of the system. The complexity of the algorithm is 90% of that of the MMSE-PIC detection algorithm, and the performance is improved by 2 dB.
【作者單位】： 電子科技大學(xué)通信抗干擾國家級(jí)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金(6150010678,61371104)
【分類號(hào)】：TN919.3
，
本文編號(hào)：2316497
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