CNN demodulation model with cascade parallel crossing for CPM signals

doi:10.19682/j.cnki.1005-8885.2024.1005

中国邮电高校学报(英文) ›› 2024, Vol. 31 ›› Issue (3): 30-42.doi: 10.19682/j.cnki.1005-8885.2024.1005

• Artificial Intelligence • 上一篇下一篇

CNN demodulation model with cascade parallel crossing for CPM signals

杨嘉晨¹,段瑞枫²,李诚驹¹

1. School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
2. Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and
Grassland Administration, Beijing 100083, China

收稿日期:2022-12-27 修回日期:2023-07-02 出版日期:2024-06-30 发布日期:2024-06-30
通讯作者: 段瑞枫 E-mail:drffighting2008@163.com
基金资助:
the Beijing Natural Science Foundation (L202003).

CNN demodulation model with cascade parallel crossing for CPM signals

Yang Jiachen, Duan Ruifeng, Li Chengju

1. School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
2. Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and
Grassland Administration, Beijing 100083, China

Received:2022-12-27 Revised:2023-07-02 Online:2024-06-30 Published:2024-06-30
Contact: Duan Ruifeng E-mail:drffighting2008@163.com
Supported by:
the Beijing Natural Science Foundation (L202003).

摘要/Abstract

摘要： The continuous phase modulation (CPM) technique is widely used in range telemetry due to its high spectral
efficiency and power efficiency. However, the demodulation performance of the traditional maximum likelihood
sequence detection (MLSD) algorithm significantly deteriorates in non-ideal synchronization or fading channels. To
address this issue, this work proposes a convolutional neural network (CNN) called the cascade parallel crossing
network (CPCNet) to enhance the robustness of CPM signals demodulation. The CPCNet model employs a multiple
parallel structure and feature fusion to extract richer features from CPM signals. This approach constructs feature
maps at different levels, resulting in a more comprehensive training of the model and improved demodulation
performance. Simulation results show that under Gaussian channel, the proposed CPCNet achieves the same bit
error rate (BER) performance as MLSD method when there is no timing error, but with 1/4 symbol period timing
error, the proposed method has 2 dB demodulation gain compared with CNN and convolutional long short-term
memory deep neural network (CLDNN). In addition, under Rayleigh channel, the BER of the proposed method is
reduced by 5% -87% compared to that of MLSD in the wide signal-to-noise ratio (SNR) region.

关键词: continuous phase modulation (CPM), convolutional neural network (CNN), maximum likelihood sequence detection (MLSD), Rayleigh
fading, timing error

Abstract: The continuous phase modulation (CPM) technique is widely used in range telemetry due to its high spectral
efficiency and power efficiency. However, the demodulation performance of the traditional maximum likelihood
sequence detection (MLSD) algorithm significantly deteriorates in non-ideal synchronization or fading channels. To
address this issue, this work proposes a convolutional neural network (CNN) called the cascade parallel crossing
network (CPCNet) to enhance the robustness of CPM signals demodulation. The CPCNet model employs a multiple
parallel structure and feature fusion to extract richer features from CPM signals. This approach constructs feature
maps at different levels, resulting in a more comprehensive training of the model and improved demodulation
performance. Simulation results show that under Gaussian channel, the proposed CPCNet achieves the same bit
error rate (BER) performance as MLSD method when there is no timing error, but with 1/4 symbol period timing
error, the proposed method has 2 dB demodulation gain compared with CNN and convolutional long short-term
memory deep neural network (CLDNN). In addition, under Rayleigh channel, the BER of the proposed method is
reduced by 5% -87% compared to that of MLSD in the wide signal-to-noise ratio (SNR) region.

Key words: continuous phase modulation (CPM), convolutional neural network (CNN), maximum likelihood sequence detection (MLSD), Rayleigh
fading, timing error

中图分类号:

TN911.7

Yang Jiachen, Duan Ruifeng, Li Chengju. CNN demodulation model with cascade parallel crossing for CPM signals[J]. The Journal of China Universities of Posts and Telecommunications, 2024, 31(3): 30-42.

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CNN demodulation model with cascade parallel crossing for CPM signals

CNN demodulation model with cascade parallel crossing for CPM signals

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