A Novel Automatic Modulation Classification Method Based on Dictionary Learning

• 作 者: Kezhong Zhang Li Xu Zhiyong Feng Ping Zhang
• 机 构: Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunicati
• 出 处: 中国通信（英文版）
• 发表日期: 2019年1期
• 关键词: modulation classification non-cooperative scenarios dictionary learning sparse c

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.