Spectrum sensing method of railway communication system based on deep neural network

YUAN Zhangfu; SONG Zongying; WANG Xingzhong; ZHOU Yiming

doi:10.11959/j.issn.1000-0801.2025157

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Spectrum sensing method of railway communication system based on deep neural network

Research and Development | 更新时间：2025-11-16

- Spectrum sensing method of railway communication system based on deep neural network
- Telecommunications Science Vol. 41, Issue 9, Pages: 108-118(2025)
- 作者机构：
  
  1.中国神华能源股份有限公司，北京 100011
  2.北京邮电大学电子工程学院，北京 100876
- 作者简介：
- 基金信息：
  
  Science and Technology Project of China Shenhua Energy Company Limited(SHGF-24-02)
- DOI：10.11959/j.issn.1000-0801.2025157
  CLC： TN929.5
- Received：12 February 2025，
  
  Revised：2025-04-30，
  
  Accepted：03 June 2025，
  
  Published：20 September 2025
- 稿件说明：
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袁张付,宋宗莹,王兴中等.基于深度神经网络的铁路通信系统频谱感知方法[J].电信科学,2025,41(09):108-118.

YUAN Zhangfu,SONG Zongying,WANG Xingzhong,et al.Spectrum sensing method of railway communication system based on deep neural network[J].Telecommunications Science,2025,41(09):108-118.
袁张付,宋宗莹,王兴中等.基于深度神经网络的铁路通信系统频谱感知方法[J].电信科学,2025,41(09):108-118. DOI： 10.11959/j.issn.1000-0801.2025157.

YUAN Zhangfu,SONG Zongying,WANG Xingzhong,et al.Spectrum sensing method of railway communication system based on deep neural network[J].Telecommunications Science,2025,41(09):108-118. DOI： 10.11959/j.issn.1000-0801.2025157.

摘要

针对当前铁路通信系统400 MHz专用频段中存在的频谱资源紧张问题，提出了一种基于深度神经网络的频谱感知方法。该方法通过联合分析信号的空-频-时联合表征，在保障主用户通信质量的前提下实现高精度频谱空穴检测。具体实现如下：首先，认知用户对接收到的感知样本进行信号处理，提取信号的能量、功率谱和循环谱特征，并拼接为信号特征矩阵，作为神经网络的输入；其次，通过特征嵌入模块对各模态特征进行深层表示；最后，在混合感知层中利用2个多层感知机（multilayer perceptron，MLP）模块分别提取各模态特征及特征间的关联性，并完成频谱判决。实验结果表明，在虚警概率为0.01、信噪比为-10 dB的仿真环境下，该方法的检测概率可达99.8%。与目前基于卷积神经网络和卷积长短期记忆深度神经网络的频谱感知方法相比，该方法的检测概率平均可提升3.23%、2.61%。所提方法为铁路异构通信系统的动态频谱接入提供了高可靠性解决方案。

Abstract

To address the spectrum scarcity issue in the 400 MHz dedicated frequency band of railway communication systems

a deep neural network（DNN）-based spectrum sensing method was proposed. By jointly analyzing the spatial-frequency-temporal joint characterization of signals

high-precision spectrum hole detection was achieved while ensuring the communication quality of primary users. The implementation procedure was conducted as follows: firstly

the received sensing samples were processed by cognitive users

where energy features

power spectral density

and cyclostationary characteristics were extracted and concatenated into a signal feature matrix as the neural network input. Subsequently

each modal feature was deeply represented through a feature embedding module. Finally

two multilayer perceptron (MLP) modules in the hybrid sensing layer were utilized to extract both intra-modal features and inter-modal correlations

followed by spectrum decision-making. Experimental results demonstrate that under simulated conditions with a false alarm probability of 0.01 and signal-to-noise ratio (SNR) of -10 dB

the proposed method achieves a detection probability of 99.8%. Compared with existing spectrum sensing methods based on convolutional neural networks (CNN) and convolutional long short-term memory deep neural networks

the detection probability was improved by 3.23% and 2.61% on average

respectively. This method provides a highly reliable solution for dynamic spectrum access in heterogeneous railway communication systems.

关键词

Keywords

references

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