脉冲噪声下基于Renyi熵的分数低阶双模盲均衡算法

针对脉冲噪声下盲均衡器难以快速收敛并有效抑制噪声的问题，该文提出一种基于Renyi熵的分数低阶双模盲均衡算法。该算法将Renyi熵与分数低阶统计量相结合并用作代价函数来更新盲均衡器权向量，利用Renyi熵提高算法的收敛速度，利用分数低阶统计量增强算法对脉冲噪声的抑制能力。为了提升系统稳健性，该文进一步提出双阈值加权判决法，通过设置双阈值并引入非线性加权函数，使得两种代价函数之间的切换更为平滑。在不同脉冲性噪声、不同信道环境下进行仿真实验，结果表明，该文算法既能有效抑制脉冲噪声，又具有较快的收敛速度。

Dual-mode Blind Equalization Algorithm Based on Renyi Entropy and Fractional Lower Order Statistics Under Impulsive Noise

To improve the convergence speed and noise suppression effects of blind equalizer under impulsive noise environment, a new dual-mode blind equalization algorithm based on Renyi entropy and fractional lower order statistics is presented. Renyi entropy and fractional lower order statistics are combined as cost functions to update the weight coefficients of the equalizer in this method, which can improve the convergence speed and enhance the ability of suppressing impulse noise. In addition, considering the robustness of system, a double-threshold based weighting decision method is proposed. By setting double thresholds and a nonlinear weighting function, the switching between two cost functions become smooth. Simulation experiments are carried out under different impulse noise and different channel conditions. The results show that the algorithm converges faster and suppresses impulse noise effectively at the same time.