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. 2024 Jul 14;24(14):4562.
doi: 10.3390/s24144562.

Reconstruction of OFDM Signals Using a Dual Discriminator CGAN with BiLSTM and Transformer

Yuhai Li  1 , Youchen Fan  2 , Shunhu Hou  1 , Yufei Niu  1 , You Fu  1 , Hanzhe Li  1
Affiliations

Reconstruction of OFDM Signals Using a Dual Discriminator CGAN with BiLSTM and Transformer

Yuhai Li et al. Sensors (Basel). .

Abstract

Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods' intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods.

Keywords: LSTM; communication signal; conditional generative adversarial network; orthogonal frequency division multiplexing; signal reconstruction; transformer.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
CGAN model architecture.
Figure 2
Figure 2
Flowchart of the OFDM baseband communication system.
Figure 3
Figure 3
Signal reconstruction model.
Figure 4
Figure 4
Improved CGAN model architecture.
Figure 5
Figure 5
Discriminator network model.
Figure 6
Figure 6
Generator network model.
Figure 7
Figure 7
Reconstruction results of the time-domain waveform map and constellation diagram when SNR was 20 dB. (Left to right modulation style is BPSK, QPSK, 16QAM). (a) Original signal time-domain waveform; (b) Reconstruction of signal time-domain waveform; (c) Original signal constellation diagram; (d) Reconstruction of signal constellation diagram.
Figure 8
Figure 8
Reconstruction results of time-domain waveform map and constellation diagram when SNR was 15 dB. (Left to right modulation style is BPSK, QPSK, 16QAM). (a) Original signal time-domain waveform; (b) Reconstruction of signal time-domain waveform; (c) Original signal constellation diagram; (d) Reconstruction of signal constellation diagram.
Figure 9
Figure 9
Reconstruction results of time-domain waveform map and constellation diagram when SNR was 10 dB. (Left to right modulation style is BPSK, QPSK, 16QAM). (a) Original signal time-domain waveform; (b) Reconstruction of signal time-domain waveform; (c) Original signal constellation diagram; (d) Reconstruction of signal constellation diagram.
Figure 10
Figure 10
Visual comparison of time-domain waveform of OFDM symbol sequence when the SNR was 20 dB. (Left to right modulation style is BPSK, QPSK, 16QAM). (a) Real part; (b) Imaginary part.
Figure 11
Figure 11
Visual comparison of OFDM signal spectrogram when the SNR was 20 dB. (Left to right modulation style is BPSK, QPSK, 16QAM).
Figure 12
Figure 12
Visual comparison of time-domain waveform of OFDM symbol sequence when the SNR was 15 dB. (Left to right modulation style is BPSK, QPSK, 16QAM). (a) Real part; (b) Imaginary part.
Figure 13
Figure 13
Visual comparison of OFDM signal spectrogram when the SNR was 15 dB. (Left to right modulation style is BPSK, QPSK, 16QAM).
Figure 14
Figure 14
Visual comparison of time-domain waveform of OFDM symbol sequence when the SNR was 10 dB. (Left to right modulation style is BPSK, QPSK, 16QAM). (a) Real part; (b) Imaginary part.
Figure 15
Figure 15
Visual comparison of OFDM signal spectrogram when the SNR was 10 dB. (Left to right modulation style is BPSK, QPSK, 16QAM).
Figure 16
Figure 16
Probability density distribution when the SNR was 20 dB. (Left to right modulation style is BPSK, QPSK, 16QAM). (a) Real part; (b) Imaginary part.
Figure 17
Figure 17
Probability density distribution when the SNR was 15 dB. (Left to right modulation style is BPSK, QPSK, 16QAM). (a) Real part; (b) Imaginary part.
Figure 18
Figure 18
Probability density distribution when the SNR was 10 dB. (Left to right modulation style is BPSK, QPSK, 16QAM). (a) Real part; (b) Imaginary part.
See this image and copyright information in PMC

References

    1. Zhao F., Jin H. Communication jamming waveform generation technology based on GAN. Syst. Eng. Electron. 2021;43:1080–1088.
    1. Zhang J.Y., Li C., Yang Y. A Study on key techniques in cognitive communication countermeasures. Radio Eng. 2020;50:619–623.
    1. Kumar A., Majhi S., Gui G., Wu H.-C., Yuen C. A Survey of Blind Modulation Classification Techniques for OFDM Signals. Sensors. 2022;22:1020. doi: 10.3390/s22031020. - DOI - PMC - PubMed
    1. Gu Y., Xu S., Zhou J. Automatic Modulation Format Classification of USRP Transmitted Signals Based on SVM; Proceedings of the 2020 Chinese Automation Congress (CAC); Shanghai, China. 6–8 November 2020; pp. 3712–3717.
    1. Chai X. Master’s Thesis. Nanchang University; Nanchang, China: 2023. Research on Key Technology of Blind Demodulation for OFDM Signals.

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