Modulation Recognition Algorithm Based on Multi-Criteria Fusion and Intelligent Decision

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Modulation Recognition Algorithm Based on Multi-Criteria Fusion and Intelligent Decision

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[1]XIA Zhaoyu,LIN Yujie,HU Chunyuan,et al.Modulation Recognition Algorithm Based on Multi-Criteria Fusion and Intelligent Decision[J].Journal of Zhengzhou University (Engineering Science),2024,45(pre):2-.[doi:10. 13705 / j. issn. 1671-6833. 2024. 04. 015]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 pre Page number: 2- Column: Public date: 2024-12-31

Title:: Modulation Recognition Algorithm Based on Multi-Criteria Fusion and Intelligent Decision

Author(s):: XIA Zhaoyu; LIN Yujie; HU Chunyuan; WU Zihao; (School of Information and Electronics, Beijing Institute of Technology, Beijing 100081 , China)

Keywords:: modulation recognition; higher-order cumulative extension; CART decision tree; Gini coefficient; multi-criteria fusion; pruning algorithm

CLC:: TN98, TN929.5

DOI:: 10. 13705 / j. issn. 1671-6833. 2024. 04. 015

Abstract:: Aiming at the requirement of high modulation order identification and the difficulty of modulation identification in low signal-to-noise ratio environment in 6G communication, a modulation recognition algorithm based on multi-criteria fusion and intelligent decision was proposed by combining artificial intelligence technology and modern signal processing technology. The algorithm was divided into two parts: multi-criteria fusion network and intelligent decision network. The multi-criteria fusion network calculated the higher-order cumulative extensions of the standard modulation signals, traversed all the potential thresholds by using local optimal solutions, and determined the judgment thresholds by Gini coefficient and the entropy of certainty gain. The intelligent decision network adopted a CART architecture to recognize the modulation format of unknown signals using the determined judgment thresholds, and the model was iterative optimized using a pruning algorithm to obtain the finally optimal decision tree, forming a modulation recognition algorithm based on multi-criteria fusion and intelligent decision making. Experimental results showed that the algorithm could accurately recognize 16QAM, 64QAM, 128QAM, 1024QAM, 2PSK, 4PSK, 8PSK, 2FSK, 4FSK at 0dB SNR, and the comprehensive recognition accuracy reached 99.4%. Compared with other methods, the modulation recognition accuracy and the types of recognizable modulation have been improved

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