时变平坦衰落SIMO信道中的信噪比盲估计

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英文篇名：SNR Blind Estimation over Time-varying Flat-fading SIMO Channels 作者：李涛 ; 许华 ; 蒋磊 英文作者：LI Tao;XU Hua;JIANG Lei;School of Information and Navigation,Air Force Engineering University; 关键词：时变平坦衰落SIMO信道 ; 信噪比盲估计 ; EM算法 ; M2M4算法 英文关键词：time-varying flat-fading SIMO channels;;SNR blind estimation;;EM algorithm;;M2M4 algorithm 中文刊名：HLYZ 英文刊名：Fire Control & Command Control 机构：空军工程大学信息与导航学院; 出版日期：2019-05-15 出版单位：火力与指挥控制 年：2019 期：v.44;No.290 基金：国家自然科学基金资助项目(61001111) 语种：中文; 页：HLYZ201905015 页数：4 CN：05 ISSN：14-1138/TJ 分类号：72-75

摘要

针对恒包络信号在时变平坦衰落单输入多输出(SIMO)信道下的信噪比盲估计问题,提出一种基于EM(Expectation-Maximization)算法的改进M2M4-EM算法。利用EM算法的迭代期望值最大化思想,推导了SIMO信道下EM算法的闭式解。为进一步改善性能,通过接收信号的二阶和四阶矩(M2M4)对EM算法进行初始化。仿真结果表明,M2M4-EM算法在较宽的信噪比范围内具有较好的估计精度和较快的收敛速度。
        An improved M2M4-EM algorithm based on EM(Expectation-Maximization)algorithm is proposed to solve the SNR blind estimation problem of constant-envelope signal in time-varying flat-fading single-input multiple-output(SIMO) channels. Based on the idea of iterative expectation maximization of the EM algorithm,the closed solution of EM algorithm in SIMO channels is deduced. In order to improve the performance of the algorithm further,the rough initial value of the parameters to be estimated are obtained by using the second and fourth moments(M2M4)of the received signal. The simulation results show that the M2M4-EM algorithm has better performance of estimated precision and faster convergece rate in the wide signal-to-noise ratio(SNR)range.

引文

[1]许华,王爱粉,杨晓宇.常规数字通信信号信噪比估计综述[J].信号处理,2013,29(6):723-733.
    [2]RICE M.Data-aided and non-data-aided maximum likelihood SNR estimators for CPM[J].IEEE Trans.Commun.,2015,63(11):4244-4253.
    [3]RAZA M A,HUSSAIN A.Maximum likelihood SNR estimation of hyper cubic signals over gaussian channel[J].IEEECommunications Letters,2016,20(1):45-48.
    [4]WU N,WANG H,KUANG J M.Maximum likelihood signal-to-noise ratio estimation for coded linearly modulated signals[J].IET Commun.,2010,4(3):265-271.
    [5]CUI L J,WANG S,CHENG S.Online SNR statistic estimation for LDPC decoding over AWGN channel using laplace propagation[C]//IEEE Global Communications Conference(GLOBECOM),Anaheim,2012:3743-3747.
    [6]GAPPMAIR W,LOPEZ-VALCARCE R,MOSQUERA C.Joint NDA estimation of carrier frequency/phase and SNRfor linearly modulated signals[J].IEEE Signal Processing Letters,2010,17(5):517-520.
    [7]WU N,WANG H,KUANG J M.Maximum likelihood signal-to-noise ratio estimation for coded linearly modulated signals[J].IET Commun,2010,4(3):265-271.
    [8]BELLILI F,MEFTEHI R,AFFES S,et al.Maximum likelihood SNR estimation of linearly-modulated signals over time-varying flat-fading SIMO channels[J].IEEE Transactions on Signal Processing,2015,63(2):441-456.
    [9]WU C F J.On the convergence properties of EM algorithm[J].Annals of Statistics,1983,11:95-103.
    [10]LOPEZ-VALCARCE R,MOSQUERA C.Sixth-order statistics-based non-data-aided SNR estimation[J].IEEE Communications Letters,2007,11(4):351-353.