TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN

Reconstruction techniques for communication signals represent a significant research focus within the field of signal processing. To overcome the difficulty and low precision in reconstructing OFDM signals, we introduce a signal reconstruction technique called TOR-GAN (Transformer-Based OFDM Signal...

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Main Authors: Yuhai Li, Youchen Fan, Shunhu Hou, Zhaojing Xu, Hongyan Wang, Shengliang Fang
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/13/4/750
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author Yuhai Li
Youchen Fan
Shunhu Hou
Zhaojing Xu
Hongyan Wang
Shengliang Fang
author_facet Yuhai Li
Youchen Fan
Shunhu Hou
Zhaojing Xu
Hongyan Wang
Shengliang Fang
author_sort Yuhai Li
collection DOAJ
description Reconstruction techniques for communication signals represent a significant research focus within the field of signal processing. To overcome the difficulty and low precision in reconstructing OFDM signals, we introduce a signal reconstruction technique called TOR-GAN (Transformer-Based OFDM Signal Reconstruction GAN). Reconstructing IQ sequences using a CNN and RNN presents challenges in capturing the correlations between two signals. To tackle this issue, the VIT (vision in transformer) approach was introduced into the discriminator network. The IQ signal is treated as a single-channel, two-dimensional image, divided into blocks of 2 × 2 pixels, with absolute position embedding added. The generator network maps the input noise to the same dimension as the IQ signal dimension × embedding vector dimension and adds two identical position embedding data points to the network learning. In the transformer network, prob sparse attention is employed as a replacement for multi-head attention to tackle the issue of high computational complexity. Finally, combined with the MLP structure, the transformer-based generator and discriminator are designed. The signal similarity evaluation index was constructed, and experiments showed that the reconstructed signal under QPSK and BPSK modulation had good reconstruction quality in the time-domain waveform, constellation diagram, and spectrogram at a high SNR. Compared with other reconstruction algorithms, the proposed algorithm improved the quality of the reconstructed signal while reducing the complexity of the algorithm.
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spelling doaj.art-3d820293fdb3480fab0d3abaaf0e2a682024-02-23T15:14:50ZengMDPI AGElectronics2079-92922024-02-0113475010.3390/electronics13040750TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GANYuhai Li0Youchen Fan1Shunhu Hou2Zhaojing Xu3Hongyan Wang4Shengliang Fang5Graduate School, Space Engineering University, Beijing 101416, ChinaSchool of Space Information, Space Engineering University, Beijing 101416, ChinaGraduate School, Space Engineering University, Beijing 101416, ChinaGraduate School, Space Engineering University, Beijing 101416, ChinaSchool of Space Information, Space Engineering University, Beijing 101416, ChinaSchool of Space Information, Space Engineering University, Beijing 101416, ChinaReconstruction techniques for communication signals represent a significant research focus within the field of signal processing. To overcome the difficulty and low precision in reconstructing OFDM signals, we introduce a signal reconstruction technique called TOR-GAN (Transformer-Based OFDM Signal Reconstruction GAN). Reconstructing IQ sequences using a CNN and RNN presents challenges in capturing the correlations between two signals. To tackle this issue, the VIT (vision in transformer) approach was introduced into the discriminator network. The IQ signal is treated as a single-channel, two-dimensional image, divided into blocks of 2 × 2 pixels, with absolute position embedding added. The generator network maps the input noise to the same dimension as the IQ signal dimension × embedding vector dimension and adds two identical position embedding data points to the network learning. In the transformer network, prob sparse attention is employed as a replacement for multi-head attention to tackle the issue of high computational complexity. Finally, combined with the MLP structure, the transformer-based generator and discriminator are designed. The signal similarity evaluation index was constructed, and experiments showed that the reconstructed signal under QPSK and BPSK modulation had good reconstruction quality in the time-domain waveform, constellation diagram, and spectrogram at a high SNR. Compared with other reconstruction algorithms, the proposed algorithm improved the quality of the reconstructed signal while reducing the complexity of the algorithm.https://www.mdpi.com/2079-9292/13/4/750communication signalsignal reconstructionorthogonal frequency division multiplexinggenerative adversarial networktransformer
spellingShingle Yuhai Li
Youchen Fan
Shunhu Hou
Zhaojing Xu
Hongyan Wang
Shengliang Fang
TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN
Electronics
communication signal
signal reconstruction
orthogonal frequency division multiplexing
generative adversarial network
transformer
title TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN
title_full TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN
title_fullStr TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN
title_full_unstemmed TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN
title_short TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN
title_sort tor gan a transformer based ofdm signals reconstruction gan
topic communication signal
signal reconstruction
orthogonal frequency division multiplexing
generative adversarial network
transformer
url https://www.mdpi.com/2079-9292/13/4/750
work_keys_str_mv AT yuhaili torganatransformerbasedofdmsignalsreconstructiongan
AT youchenfan torganatransformerbasedofdmsignalsreconstructiongan
AT shunhuhou torganatransformerbasedofdmsignalsreconstructiongan
AT zhaojingxu torganatransformerbasedofdmsignalsreconstructiongan
AT hongyanwang torganatransformerbasedofdmsignalsreconstructiongan
AT shengliangfang torganatransformerbasedofdmsignalsreconstructiongan