Bibliographic Details
| Title: |
A CNN‐BiLSTM–Based Deep Learning Model for CPM Signal Detection. |
| Authors: |
He, Yang1 (AUTHOR), Cao, Ning2 (AUTHOR), Hu, Can2 (AUTHOR), Lu, Hao1 (AUTHOR) luhao@hhu.edu.cn |
| Source: |
Electronics Letters (Wiley-Blackwell). Jan2025, Vol. 61 Issue 1, p1-6. 6p. |
| Subjects: |
Convolutional neural networks, Continuous phase modulation, Detection algorithms, Deep learning, Signal processing, Long short-term memory, Mathematical optimization |
| Abstract: |
This letter proposes a convolutional neural network–bidirectional long short‐term memory (CNN‐BiLSTM) architecture for continuous phase modulation (CPM) signal detection by optimising the extraction of time‐frequency features and temporal dependencies with reduced complexity. It significantly outperforms the existing maximum likelihood sequence detection (MLSD) and CNN with fully connected layer (CNN‐FC) detectors in higher‐order modulation and multipath scenarios, achieving a 97.99% parameter reduction compared to CNN‐FC. Numerical results confirm its exceptional balance of detection performance and computational efficiency, making it ideal for complex channels and resource‐constrained systems. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
