[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),2025,46(04):55-61.[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),2025,46(04):55-61.[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:
46
期数:
2025年04期
页码:
55-61
栏目:
出版日期:
2025-07-10

文章信息/Info

Title:
Modulation Recognition Algorithm Based on Multi-criteria Fusion and Intelligent Decision
文章编号:
1671-6833(2025)04-0055-07
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 100081
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:
TN98TN929. 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。 所提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 0 dB 信噪比情况下,所提算法能够对 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、可识别调制体制均有提升。
Abstract:
Aiming to meet the requirement of modulation recognition in high order and to solve the difficulty of modulation recognition 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 0 dB SNR, and the comprehensive recognition accuracy reached 99. 4%. Compared with other methods, the modulation recognition accuracy and the types of recognizable modulation were improved.

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更新日期/Last Update: 2025-07-13