基展开模型下的时变信道半盲均衡算法

Semi-blind Equalizer Estimation for Time-Varying Channels Based on Basis Expansion Model

提出了一种针对时变信道的全新半盲均衡算法。时变信道由经典的复指数基展开模型描述,相应地,算法基于该模型专用的时变线性均衡器设计。盲均衡算法由联合独立分量分析和软判决引导的代价函数构建,并采用牛顿迭代的方式工作,以更好消除码间干扰。除此之外,复指数基展开模型的特殊结构经充分利用大大简化了牛顿迭代计算。有限的训练符号用于初始化均衡器系数和判断迭代是否继续。与现有的时变信道处理算法相比,本文算法较好地兼顾了均衡效果和信道利用率,具有均衡效果好,抗噪声能力强等优点。仿真验证了算法的有效性。

A novel semi-blind equalization method is presented for time-varying channel. The complex exponential basis expansion model is introduced to describe the time-varying channel taps. Therefore, the method is designed based on the linear time-varying equalizer. The new method adopts the Newton iterative based equalization algorithm which combines the independent component analysis criterion with soft-decision directed criterion to remove the inter-symbol interference. Furthermore, the method reduces the iterative computation burden by using the complex exponential basis expansion model structure. A small number of training symbols are utilized to provide a rough initial estimate of the equalizer/s weight vector and decide if the iteration should go on. Compared with the existing ones, the proposed algorithm considers both channel spectral efficiency and equalization processing, leading to a better equalization and more robust anti-noise ability. Simulation results prove its efficiency.