一种复杂星座信号的盲均衡新方法

摘要

基于高阶统计量(HOS)盲均衡算法虽可适用于单人单出(SISO)系统,但HOS算法均依赖大数据量而无法满足高速信号传输的时变要求,该类算法对于高阶正交幅度调制(QAM)信号系统的盲均衡能力偏弱.该文在较小数据量的前提下,提出一种适用于SISO系统的高阶QAM信号盲均衡的新算法.算法运用支持向量回归框架,根据有序风险最小化原则,通过构造由恒模算法和星座匹配误差代价函数联合组成的新经验风险项和引入ε-不敏感损失函数的方法,构造出一个新的代价函数,从而将高阶QAM信号系统的盲均衡问题转化为求解无约束的优化问题.最后采用64-QAM信号进行仿真和分析,仿真结果发现无论从算法运算代价和对数据量的要求上均优越于现有HOS盲均衡算法.%Blind equalization algorithms based on high order statistics (HOS) are suitable to single input single output (SISO) systems,but HOS algorithms need large amount of data and cannot meet the time-varying requirements in high-speed signal transmission systems and have not enough ability to detect blindly high-order quadrature amplitude modulation (QAM) signals. A novel cost function is constructed using support vector regression by structural risk minimization principle whose empiric risk is composed by the cost functions of constant modulus algorithm (CMA) and constellation match error (CME), and the epsilon-insensitive loss function is adopted,all of which transform the blind equalization problems of high-order QAM signals into solving an unconstrained optimization problem.Finally,64-QAM signal is used to simulate and analyze the performance of the novel algorithm and the results snow that the burden of algorithm and the data requirements are superior to those existing HOS algorithms.