arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1012.5327cs.ITcs.PFmath.ITstat.ML
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基于概率分布距离函数的高效调制电平分类

Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions

Paulo Urriza, Eric Rebeiz, Przemysław Pawełczak, Danijela Čabrić

摘要:

提出了一种基于概率分布距离函数的调制等级分类方法。所提出的方法使用修改的Kuiper和Kolmogorov-Smirnov距离,以实现低计算复杂性,并优于基于累积量和拟合优度检验的最新方法。我们推导出所提出的MLC方法的理论性能,并通过仿真验证。最好的分类精度,AWGN下的SNR失配和相位抖动,实现与建议MLC方法使用Kuiper距离。

英文摘要:

We present a novel modulation level classification (MLC) method based on probability distribution distance functions. The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and goodness-of-fit tests. We derive the theoretical performance of the proposed MLC method and verify it via simulations. The best classification accuracy, under AWGN with SNR mismatch and phase jitter, is achieved with the proposed MLC method using Kuiper distances.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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