基于稀疏贝叶斯联合估计的多天线信号判决反馈频域均衡方法

Decision Feedback Frequency-Domain Equalization of Multi-Antenna Signals Based on Sparse Bayesian Joint Estimation

相比于经典解耦处理的前馈均衡方法,判决反馈均衡具有更优的性能,但现有方法主要针对收发静止或理想载波同步的情形.本文提出了一种针对未理想载波同步多天线信号的判决反馈频域均衡方法.文中首先基于最小均方误差准则推导了多天线联合判决反馈频域均衡优化模型,针对模型中残余频偏、信道系数、噪声方差等参数难以直接求解问题,给出了基于稀疏贝叶斯的迭代估计方法,并通过近似计算,给出了所有参数估计式的闭式解,避免了网格搜索带来的计算量负担.所提方法综合考虑多天线信号接收处理不同环节之间的关联性,采用迭代处理方法进行联合寻优.仿真实验表明,所提方法能够有效抑制信道衰落的影响,与现有方法相比,在接收单元数目较少和信噪比较高时优势体现更加明显.

Compared with the classical decoupling feedforward equalization method,the decision feedback equaliza⁃tion(DFE)method has better performance.However,current methods mainly aim at the case of static or ideal carrier syn⁃chronization.In this paper,a new frequency domain DFE method is proposed for carrier unsynchronized multi-antenna sig⁃nals.Firstly,the optimization model of multi-antenna joint frequency domain DFE is derived based on the minimum mean square error(MMSE)criterion.Aiming at the difficult problem of direct estimating the residual frequency offsets,channel coefficients and noise variances,an iterative estimation scheme based on sparse Bayesian learning(SBL)is given.Closedform solutions of all parameter estimation formulas are given through approximate calculation to avoid the computational burden caused by grid search.The proposed method comprehensively considers the correlation among different steps of multi-antenna signal reception and processing,and adopts iterative processing method for joint optimization.Simulation re⁃sults show that the proposed method can effectively suppress the influence of channel fading,and the advantages are more obvious when the number of receiving units is small and the signal-to-noise ratio(SNR)is high compared with the existing methods.