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

Kai SHAO; Xuyang FU; Guangyu WANG

doi:10.11999/JEIT201064

Volume 43 Issue 11

Nov. 2021

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Kai SHAO, Xuyang FU, Guangyu WANG. Intelligent Multi-carrier Waveform Modulation System: Signal Generation and Recognition[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3096-3104. doi: 10.11999/JEIT201064

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Kai SHAO, Xuyang FU, Guangyu WANG. Intelligent Multi-carrier Waveform Modulation System: Signal Generation and Recognition[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3096-3104. doi: 10.11999/JEIT201064

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

doi: 10.11999/JEIT201064

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

Received Date: 2020-12-18
Rev Recd Date: 2021-08-13

Available Online: 2021-08-26

Publish Date: 2021-11-23

Abstract

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.

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References(18)

References

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