摘要
Automatic Modulation Classification(AMC) is an important technology used to recognize the modulation type.A dictionary set was trained via signals with known modulation schemes in cooperative scenarios.Then we classify the modulation scheme of the signals received in the non-cooperative environment according to its sparse representation.Furthermore,we proposed a novel approach called Fast Block Coordinate descent Dictionary Learning(FBCDL).Moreover,the convergence of FBCDL was proved and we find that our proposed method achieves lower complexity.Experimental results indicate that our proposed FBCDL achieves better classification accuracy than traditional methods.
Automatic Modulation Classification(AMC) is an important technology used to recognize the modulation type.A dictionary set was trained via signals with known modulation schemes in cooperative scenarios.Then we classify the modulation scheme of the signals received in the non-cooperative environment according to its sparse representation.Furthermore,we proposed a novel approach called Fast Block Coordinate descent Dictionary Learning(FBCDL).Moreover,the convergence of FBCDL was proved and we find that our proposed method achieves lower complexity.Experimental results indicate that our proposed FBCDL achieves better classification accuracy than traditional methods.
基金
supported in part by the National Natural Science Foundation of China with grants 61525101,91746301,61631003,61601055
the Shenzhen Fundamental Research Fund with grant KQTD2015033114415450
