使用对数乘积分数矩（LP-FM）准则在加性脉冲噪声中进行盲自适应均衡
Blind Adaptive Equalization in Additive Impulsive Noise Using the Logarithmic Product Fractional-Moment (LP-FM) Criterion
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中文总结 AI 辅助
研究在加性白色脉冲噪声中盲抑制符号间干扰问题，提出LP-FMS准则并推导NBEA-SAS算法，仿真表明该算法在中等和高度脉冲SαS噪声条件下收敛快且稳态残余符号间干扰底限可比，为脉冲噪声环境盲自适应均衡提供强大框架。
中文摘要 AI 辅助
研究了在由对称α稳定（SαS）分布建模的加性白色脉冲噪声中符号间干扰的盲抑制问题。在约束优化框架中，通过将互补分数矩统计与对数归一化相结合，提出了一种新颖的对数乘积分数矩统计（LP-FMS）准则。基于此准则，利用随机梯度上升、递归分数矩估计和布斯冈一致约束自适应，推导了一种用于对称α稳定噪声的归一化盲均衡算法（NBEA-SAS）。对分数间隔多径微波信道上的64-APSK信号进行仿真，结果表明，在中等和高度脉冲SαS噪声条件下，该算法收敛速度比FLOS-CMA、RAW-CMA和NBEA-GG更快，同时实现了可比的稳态残余符号间干扰底限。结果表明，所提出的LP-FMS准则为脉冲噪声环境中的盲自适应均衡提供了一个强大的框架。
英文摘要
The blind mitigation of inter-symbol interference in additive white impulsive noise modeled by a symmetric $α$-stable (S$α$S) distribution is investigated. A novel logarithmic product fractional-moment statistics (LP-FMS) criterion is proposed by combining complementary fractional-moment statistics with logarithmic normalization in a constrained optimization framework. Based on this criterion, a normalized blind equalization algorithm for symmetric alpha-stable noise (NBEA-SAS) is derived using stochastic gradient ascent with recursive fractional-moment estimation and Bussgang-consistent constrained adaptation. Simulation results for $64$-APSK signaling over fractionally spaced multipath microwave channels show that the proposed algorithm converges faster than FLOS-CMA, RAW-CMA, and NBEA-GG while achieving a comparable steady-state residual intersymbol interference floor under both moderately and highly impulsive S$α$S noise conditions. The results demonstrate that the proposed LP-FMS criterion provides a robust framework for blind adaptive equalization in impulsive noise environments.
