基于采样分布距离的调制分类最优判别函数
Optimal Discriminant Functions Based On Sampled Distribution Distance for Modulation Classification
摘要：
在这封信中，我们推导出调制分类的最佳判别函数的基础上采样的分布距离。所提出的方法分类各种候选星座使用低复杂度的方法的基础上，在特定的测试点沿累积分布函数的分布距离。该方法基于贝叶斯决策准则，在给定一组测试点的情况下，渐近地提供可能的最小分类误差。测试点位置也进行了优化，以提高分类性能。该方法提供了显着的增益超过现有的方法，也使用的信号特征的分布。
英文摘要：
In this letter, we derive the optimal discriminant functions for modulation classification based on the sampled distribution distance. The proposed method classifies various candidate constellations using a low complexity approach based on the distribution distance at specific testpoints along the cumulative distribution function. This method, based on the Bayesian decision criteria, asymptotically provides the minimum classification error possible given a set of testpoints. Testpoint locations are also optimized to improve classification performance. The method provides significant gains over existing approaches that also use the distribution of the signal features.
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