• Semantic Communications
• Source and channel coding
• Physical layer signal processing for wireless communication

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News

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● NEW [2022-12] 2 papers accepted by 2023 WCNC.

  • Variational Speech Waveform Compression to Catalyze Semantic Communications ( link )

● [2022-08] 2 papers accepted by 2022 GLOBECOM.

  • Resolution-Adaptive Source-Channel Coding for End-to-End Wireless Image Transmission ( link )
  • Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission ( link )

Papers

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Variational Speech Waveform Compression to Catalyze Semantic Communications

We propose a novel neural waveform compression method to catalyze emerging speech semantic communications. By introducing nonlinear …
PDF IEEE WCNC
Variational Speech Waveform Compression to Catalyze Semantic Communications

Adaptive Semantic Communications: Overfitting the Source and Channel for Profit

Most semantic communication systems leverage deep learning models to provide end-to-end transmission performance surpassing the …
PDF arXiv
Adaptive Semantic Communications: Overfitting the Source and Channel for Profit

Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission

As one novel approach to realize end-to-end wireless image semantic transmission, deep learning-based joint source-channel coding (deep …
PDF GLOBECOM
Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission

Resolution-Adaptive Source-Channel Coding for End-to-End Wireless Image Transmission

The recent deep learning-based joint source-channel coding (deep JSCC) framework has shown superior performance on end-to-end wireless …
GLOBECOM
Resolution-Adaptive Source-Channel Coding for End-to-End Wireless Image Transmission

Versatile Semantic Coded Transmission over MIMO Fading Channels

Semantic communications have shown great potential to boost the end-to-end transmission performance. To further improve the system …
PDF arxiv
Versatile Semantic Coded Transmission over MIMO Fading Channels

Wireless Deep Speech Semantic Transmission

In this paper, we propose a new class of high-efficiency semantic coded transmission methods for end-to-end speech transmission over …
PDF arxiv
Wireless Deep Speech Semantic Transmission
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Contact Us

Key Laboratory of Universal Wireless Communications, Ministry of Education

  • daijincheng@bupt.edu.cn
  • Xitucheng Road 10, Haidian District, Beijing 100876
  • 710, Scientific Research Building, Beijing University of Posts and Telecommunications

Team Members

Who are doing the work

Supervisor

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Kai Niu

Professor

information theory, polar codes, intelligent signal processing

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Jincheng Dai

Supervisor

Semantic communications, source and channel coding, machine learning for communications, Semantic communication

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Zhongwei Si

Associate Professor

wireless communication, information theory, data mining

Ph.D Student

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Sixian Wang

Ph.D Student

semantic communications, source and channel coding, computer vision

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Kailin Tan

Ph.D Student

semantic communications, source and channel coding, machine learning

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Shengshi Yao

Ph.D Student

semantic communications, source and channel coding, computer vision

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Weijie Yue

Ph.D Student

semantic communications, talking-head generation