针对单输入多输出系统下稀疏信道均衡问题,提出了一种新的基于最大似然准则的频域迭代均衡算法.首先将多天线联合均衡问题建模为非完整观测数据集下频域信号序列的最大似然估计问题,利用期望最大化算法进行近似迭代求解,最终得到各个单频信号加权求和形式的均衡输出表达式.在每次迭代过程中,算法依次完成均衡输出的更新和信道参数联合条件后验分布的更新.考虑到信道固有的稀疏特性,在求解信道参数联合条件后验时,引入具有稀疏促进作用的先验分布对信道系数加以约束,使用稀疏贝叶斯学习迭代求解信道参数联合条件后验.仿真结果表明,本文算法具有较好的收敛特性和稳态性能,在中高信噪比条件下可以获得接近信道已知条件下的稳态系统误符号率性能.%Aiming at the equalization problem of sparse channels in single-input multiple-output (SIMO) systems,a new iterative frequency-domain equalization algorithm is proposed based on the maximum likelihood(ML) criterion.The e-qualization of multiple signals is first modeled as the maximum likelihood frequency-domain signal sequence estimation from incomplete observations,and approximately solved by means of the expectation-maximization (EM) algorithm in an itera-tive manner.Analytic expression of the equalization output is finally obtained in the form of weighted summation of each discrete-frequency signals.In each iteration,the proposed scheme alternates between equalization output update and channel posterior distribution update.During the later step,the inherent sparse nature of the channels is exploited by employing sparse promoting prior distributions.Then,the sparse Bayesian learning iterative inference method is applied to the proposed model in order to obtain joint conditional posterior distribution of the channel parameters.Simulation results show that the proposed scheme has a good convergence and steady-state performance,and approaches the steady-state symbol error rate(SER) with known channel parameters,at moderate and high signal-to-noise ratio (SNR) values.
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