为了提高加权多模算法的盲均衡性能,提出基于加权因子非线性变化的改进加权多模盲均衡算法.新算法构造了均方误差和加权因子的非线性函数关系,提高了收敛速度,增强了算法对不同信噪比的适应能力.在算法收敛过程中,加权因子的值随着均方误差的减小逐渐增大,从而动态地调整算法的模值,使得误差模型越来越精确地匹配信号星座图,达到降低稳态均方误差的目的.理论分析和仿真结果表明,提出的算法降低了稳态均方误差,提高了收敛速度.%In order to improve the blind equalization performance of the weighted multimode algorithm, an improved weighted multi-modulus blind equalization algorithm based on a non-linear function of weighting factor was proposed in this paper. The new algorithm used a nonlinear relationship between the mean square error and the weighting factor to improve the speed of convergence and improve the adaptive capacity to different signal-to-noise ratio. In the converging process of the algorithm, with the mean square error decreased, the value of the weighting factor increased gradually. It adjusted the modulus value of the algorithm dynamically, and made the error model match with the signal constellation accurately to reduce the steady-state mean square error. The theoretical analysis and simulation results show that the proposed algorithm reduces the steady-state mean square error, and improves the convergence rate.
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