基于组合代价函数的双模式盲均衡新算法

以常数模和判决引导准则设计的双模式盲均衡算法可显著提高均衡性能，目前已有双模式盲均衡算法均需设置切换参数且切换参数选择和设定缺乏理论依据.为解决双模式盲均衡算法中切换参数难以确定的问题，提出来一种组合代价函数的双模式盲均衡新算法.利用常数模和判决引导准则通过加权设计代价函数，在盲均衡器更新过程中自适应调节权值实现算法由常数模算法向判决引导算法的切换，避免了在双模式算法中设计切换参数，提高了算法的泛化性能.为克服常数模算法相位盲的缺点，在虚实分开改进的常数模算法基础上优化组合代价函数以及盲均衡器更新算法的设计，进一步提高了算法收敛性能.仿真结果证明，组合代价函数双模式盲均衡新算法可充分发挥常数模算法和判决引导算法的优点，具有更快的收敛速度和更小的稳态剩余误差.

A New Dual Mode Blind Equalization Algorithm Based on Combination Cost Function

Although the dual mode blind equalization algorithm based on constant modulus and decision directed (DD) criterion can significantly improve the performance of equalization,the dual mode blind equalization algorithms at present need to set up the switching parameters which lack of theoretical basis.To solve the problem that the switching parameters are difficult to be determined in the dual mode blind equalization algorithm,a new dual mode blind equalization algorithm with combination cost function is proposed.The combination cost function is obtained by using the weighted constant modulus and DD cost function,and the weight is adaptively adjusted during the blind equalizer update process to switch from constant modulus algorithm (CMA) to DD algorithm,which avoids the design of the switching parameters in the dual mode algorithm,and the generalization performance can be improved.To overcome the shortcoming of phase blindness of CMA,the proposed algorithm is optimized based on modified CMA (MCMA),which can further improve the convergence performance.The simulation results show that the proposed dual mode blind equalization algorithm can take full advantages of CMA and DD algorithm,which has faster convergence rate and lower steady state error.