基于5G的通信信号调制识别系统设计  

Design of a Measurement System for Modulation Recognition Based on 5G

作　　者：李建生 LI Jiansheng(Shenzhen Keweitai Enterprise Development Co.,Ltd.,Shenzhen 518000,China)

机构地区：[1]深圳市科卫泰实业发展有限公司,广东深圳518000

出　　处：《通信电源技术》2024年第22期174-176,共3页Telecom Power Technology

摘　　要：文章设计了一种基于5G的通信信号调制识别系统,针对5G复杂信号环境下的实时性和准确性需求,采用深度学习方法实现对5G信号的高效识别。系统包括前端信号捕获与预处理、信号分析与特征提取以及调制识别与报告生成3个核心模块。通过使用改进的循环平稳性分析方法提取关键特征,并结合卷积神经网络(Convolutional Neural Network,CNN)与长短时记忆(Long Short-Term Memory,LSTM)网络进行智能分类,实现了对二进制相移键控(Binary Phase Shift Keying,BPSK)、正交相移键控(Quadrature Phase Shift Keying,QPSK)、16阶正交幅度调制(16-Quadrature Amplitude Modulation,16-QAM)、64-QAM等调制方式的高精度识别。实验结果显示,即使在低信噪比环境下,系统仍能保持较高的识别率,处理时延满足5G低时延要求,为5G通信系统的优化和智能化提供了技术支持。This article designs a modulation recognition system for 5G.To meet the demands of real-time performance and accuracy in complex 5G signal environments,deep learning methods are employed to achieve efficient recognition of 5G signals.The system comprises three core modules:front-end signal capture and preprocessing,signal analysis and feature extraction,as well as modulation recognition and report generation.By using an improved method of cyclostationary feature analysis to extract key features,and combining Convolutional Neural Networks(CNN)with Long Short-Term Memory(LSTM)networks for intelligent classification,high-precision recognition of modulations such as Binary Phase Shift Keying(BPSK),Quadrature Phase Shift Keying(QPSK),16-Quadrature Amplitude Modulation(16-QAM),and 64-QAM is realized.Experimental results show that the system maintains a high recognition rate even under low signal-to-noise ratio conditions,and its processing latency meets the low-latency requirements of 5G,providing technical support for the optimization and intelligence of 5G communication systems.

关 键 词：5G 调制识别 深度学习 卷积神经网络(CNN) 长短时记忆(LSTM) 

分 类 号：TN929.5[电子电信—通信与信息系统]