In the context of Automatic Modulation Classification (AMC), some recent works have utilized multiple features to train the neural network. With an ultimate aim to develop a systematic approach to select the most diverse and unique features, we propose and demonstrate a novel method to select the most diverse $m\atopwithdelims ()2$ features from a larger feature set. Bhattacharyya distance metric for the dissimilarity between two probability distributions is utilized to select the features with the highest distance for all modulation pairs within a test pool. The proposed approach is analyzed for three different neural networks based classifiers, amidst AWGN and frequency-selective fading channels. A substantial reduction in computational complexity is achieved with an acceptable compromise on the classification performance.

中文翻译：

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基于 Bhattacharyya 距离的神经网络自动调制分类新特征选择方法

在自动调制分类 (AMC) 的背景下，最近的一些工作利用多个特征来训练神经网络。为了开发一种系统方法来选择最多样化和独特的特征，我们提出并展示了一种从更大的特征集中选择最多样化的 $m\atopwithdelims ()2$ 特征的新方法。用于两个概率分布之间差异的 Bhattacharyya 距离度量用于为测试池中的所有调制对选择具有最高距离的特征。在 AWGN 和频率选择性衰落信道中，针对三种不同的基于神经网络的分类器分析了所提出的方法。通过对分类性能进行可接受的折衷，可以显着降低计算复杂度。