[1]夏兆宇,林玉洁,胡春源,等.基于多准则融合与智能决策的调制识别算法[J].郑州大学学报(工学版),2024,45(pre):2.[doi:10. 13705 / j. issn. 1671-6833. 2024. 04. 015]
　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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基于多准则融合与智能决策的调制识别算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年pre
页码:
2
栏目:
出版日期:
2024-12-31

文章信息/Info

Title:
Modulation Recognition Algorithm Based on Multi-Criteria Fusion and Intelligent Decision
作者:
夏兆宇; 林玉洁; 胡春源; 吴梓豪
(北京理工大学 信息与电子学院,北京 1 00081)
Author(s):
XIA Zhaoyu;  LIN Yujie;  HU Chunyuan;  WU Zihao
(School of Information and Electronics, Beijing Institute of Technology, Beijing 100081 , China)
关键词:
调制识别; 高阶累积延伸量; CART决策树; 基尼系数; 多准则融合; 剪枝算法
Keywords:
modulation recognition;  higher-order cumulative extension;  CART decision tree;  Gini coefficient;  multi-criteria fusion;  pruning algorithm
分类号:
TN98, TN929.5
DOI:
10. 13705 / j. issn. 1671-6833. 2024. 04. 015
文献标志码:
A
摘要:
针对6G通信信号调制识别阶数要求高、低信噪比环境调制识别难的问题，结合人工智能技术与现代信号处理技术，提出一种基于多准则融合与智能决策的调制识别算法。该算法分为多准则融合网络与智能决策网络两部分。多准则融合网络计算标准调制信号的高阶累积延伸量，采用局部最优解方式遍历所有潜在门限，以基尼系数和确定度增熵确定判决门限。智能决策网络采用CART型架构，以判决门限为标准对未知信号的调制体制进行识别，并使用剪枝算法对模型迭代优化，最终得到最优决策树，形成基于多准则融合与智能决策的调制识别算法。实验结果表明，在0dB信噪比情况下，算法能够对16QAM、64QAM、128QAM、1024QAM、2PSK、4PSK、8PSK、2FSK、4FSK进行精准识别，综合识别率达到99.4%。与其他方法对比，调制体制综合识别率、可识别调制体制种类均有提升。
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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备注/Memo

备注/Memo:
收稿日期：2024-03-01；修订日期：2024-04-10基金项目：国家重点研发计划资助（2023YFF0717400）；国家自然科学基金项目（62001030）通信作者：林玉洁（1989—），女，山东威海人，北京理工大学实验师，博士，主要从事无线通信研究，E-mail：linyujie@bit.edu.cn
更新日期/Last Update: 2024-10-10