邵凯^{1, 2, 3,,},
付旭阳^{1, 2},
王光宇^{1, 2, 3}
1.重庆邮电大学通信与信息工程学院 重庆 400065
2.移动通信技术重庆市重点实验室 重庆 400065
3.移动通信教育部工程研究中心 重庆 400065

详细信息

作者简介:邵凯：男，1977年生，副教授，研究方向为新型多载波调制技术、新型多址接入技术
付旭阳：男，1995年生，硕士生，研究方向为AI在无线通信中的应用
王光宇：男，1964年生，教授，研究方向为新型多载波调制技术、新型多址接入技术

通讯作者:邵凯　shaokai@cqupt.edu.cn

中图分类号:TN911

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被引次数:0

出版历程

收稿日期:2020-12-18
修回日期:2021-08-13
网络出版日期:2021-08-26
刊出日期:2021-11-23

Intelligent Multi-carrier Waveform Modulation System: Signal Generation and Recognition

Kai SHAO^{1, 2, 3,,},
Xuyang FU^{1, 2},
Guangyu WANG^{1, 2, 3}
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Chongqing Key Laboratory of Mobile Communications Technology, Chongqing 400065, China
3. Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing 400065, China


摘要
摘要:移动通信应用场景逐渐呈现复杂化和多变化趋势，很难有一种普适性传输波形满足所有通信需求，这对多种波形的配合与协作提出了较高的要求。该文提出一种适用于复杂场景下的智能化多载波波形调制系统，发送端可通过波形激活因子选择产生合适的传输波形，接收端将不同波形信号的I/Q分量作为自适应因子，使用主成分分析法处理数据后送入智能波形识别网络(IWR-Net)完成信号的识别。所提系统融合深度学习网络，具有较为统一的硬件架构。仿真结果表明，所提方案在5G多场景下对不同发送波形识别准确率最高可达98.2%，并且在不同测试环境中具有良好的泛化性能。
关键词:智能化多载波波形调制系统/
多载波波形识别/
深度学习/
智能波形识别网络
Abstract:Mobile communication applications scenarios are becoming complexity and diversity. It is difficult to have a universal transmission waveform to meet all communication needs, which puts forward high requirements for the coordination and collaboration of multiple waveforms. In this paper, an intelligent multi-carrier waveform modulation system is proposed for complex scenarios, the sending end can select a suitable transmission waveform by the waveform activation factor, the receiving end will take the I/Q component of different waveform signals as an adaptive factor, and use the main component analysis method to process the data and feed it into the Intelligent Waveform Recognition Network (IWR-Net) to complete the identification of the signal. The proposed system is integrated with deep learning network and has a more unified hardware architecture. The simulation results show that the accuracy of different send waveform recognition can be as high as 98.2% in 5G multi-scenes, and it has good generalization performance in different test environments.
Key words:Intelligent multi-carrier waveform modulation system/
Multi-carrier waveform recognition/
Deep learning/
Intelligent Waveform Recognition Networks (IWR-Net)



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