基于SRM准则的恒模盲均衡器

Constant Modulus Blind Equalizer Based on SRM Rule

针对在有限样本条件下恒模算法无法保证代价函数的经验风险与期望风险收敛到一致的问题,提出了一种基于结构风险最小化(SRM)准则的恒模盲均衡器(Structural risk minumum-contant model blind equalier,SRM-CMBE)。该方法利用信号的恒模特性,在高维特征空间中以SRM为准则构造代价函数,采用核方法实现计算,并结合Kumar快速算法和静态迭代学习算法在线跟踪信道,能够在小样本条件下有效保证代价函数的经验风险收敛到期望风险。通过仿真实验,并与标准恒模盲均衡器(Constant model blind equalizer,CMBE)和修正的恒模盲均衡器(Modified-constant model blind equalizer,M-CMBE)进行比较,结果证明该方法的非线性均衡性能最佳。

A constant modulus blind equalizer is presented based on structure risk minimum （SRM） rule for assuring the experience risk of the cost function convergence to the expectation risk of the cost function. After constructing the constant modulus cost function based on SRM rule in high dimension feature space, the method computes the cost function by kernel technique, and traces the channel by Kumar algorithm and static iterative learning algorithm. The constant modulus blind equalizer （CMBE） and the modified constant modulus blind equalizer （M-CMBE） are compared by the simulation. The result shows that the method has much better performance on the nonlinear equalization.