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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MDPI AG
2024-02-01
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Series: | Electronics |
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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. |
first_indexed | 2024-03-07T22:34:23Z |
format | Article |
id | doaj.art-3d820293fdb3480fab0d3abaaf0e2a68 |
institution | Directory Open Access Journal |
issn | 2079-9292 |
language | English |
last_indexed | 2024-03-07T22:34:23Z |
publishDate | 2024-02-01 |
publisher | MDPI AG |
record_format | Article |
series | Electronics |
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 |
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