利用确定性信道盲估计改善DFE均衡器的收敛特性

针对一种全盲的判决反馈均衡器(DFE)进行研究,它对快速时变信道的均衡非常有效。利用基于二阶统计量(SOS)的子空间法来得到关于信道系数的确定性估计,然后利用这个估计值求出DFE抽头系数在MMSE意义上的最优解,以此进行DFE的初始化。相比传统的利用周期性训练序列初始化DFE的方法,文章提出的方法不仅节省了带宽资源,而且对于信道的突发性时变能够自适应地重新初始化,从而避免了DFE均衡器的病态收敛。

Improving the convergence property of DFE using the deterministic blind channel estimation

A blind decision feedback equalizer (DFE) algorithm was proposed. The subspace method which was based on the second-order statistics (SOS) was employed to give the parametric estimation of channels unsupervised. The DFE was initialized optimally in the sense of MMSE using the estimated channel coefficients. In the comparison with the conventional DFE with training sequence, the proposed algorithm has meaningful advantages in coping with the abrupt time-variance of channels by re-initialization during communication. The ill-convergence of DFE is avoided.