CNN demodulation model with cascade parallel crossing for CPM signals  

作　　者：Yang Jiachen Duan Ruifeng Li Chengju 

机构地区：[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

出　　处：《The Journal of China Universities of Posts and Telecommunications》2024年第3期30-42,共13页中国邮电高校学报（英文版）

基　　金：Supported by the Beijing Natural Science Foundation (L202003)。

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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN911.3[自动化与计算机技术—控制科学与工程]