Modulation recognition algorithm based on multi criteria fusion and intelligent decision-making
[1]Zhaoyu Xia,Yujie Lin,Chunyuan Hu,et al.Modulation recognition algorithm based on multi criteria fusion and intelligent decision-making[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:
2026-01-10
- Title:
- Modulation recognition algorithm based on multi criteria fusion and intelligent decision-making
- Keywords:
-
modulation recognition' target='_blank'>">modulation recognition; higher-order cumulative extension; CART decision tree; Gini coefficient;multi-criteria fusion' target='_blank'>">multi-criteria fusion; pruning algorithm
- CLC:
- TN98 TN929.5
- 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 al gorithm based on multi-criteria fusion and intelligent decision was proposed by combining artificial intelligence tech nology and modern signal processing technology. The algorithm was divided into two parts. Multi-criteria fusion net work and intelligent decision network. The multi-criteria fusion network calculated the higher-order cumulative ex tensions 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 deci sion network adopted a CART architecture to recognize the modulation format of unknown signals using the deter mined judgment thresholds, and the model was iterative optimized using a pruning algorithm to obtain the finally op timal decision tree, forming a modulation recognition algorithm based on multi-criteria fusion and intelligent deci sion 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 accu racy reached 99. 4%. Compared with other methods, the modulation recognition accuracy and the types of recogniz able modulation were improved.
- References:
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Last Update:
2024-05-27
