Predictive and Adaptive Deep Coding for Wireless Image Transmission in Semantic Communication

Semantic communication is a newly emerged communication paradigm that exploits deep learning (DL) models to realize communication processes like source coding and channel coding. Recent advances have demonstrated that DL-based joint source-channel coding (DeepJSCC) can achieve exciting data compress...

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Veröffentlicht in:	IEEE transactions on wireless communications Jg. 22; H. 8; S. 1
Hauptverfasser:	Zhang, Wenyu, Zhang, Haijun, Ma, Hui, Shao, Hua, Wang, Ning, Leung, Victor C. M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Bandwidths Codes Communication Compression ratio Data compression Data models Encoding Image coding Image communication Image compression Image transmission joint source channel coding Keywords: Adaptive code length Noise Noise prediction Optimization quality prediction semantic communication Semantics Signal to noise ratio Wireless communication wireless image transmission
ISSN:	1536-1276, 1558-2248
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Abstract	Semantic communication is a newly emerged communication paradigm that exploits deep learning (DL) models to realize communication processes like source coding and channel coding. Recent advances have demonstrated that DL-based joint source-channel coding (DeepJSCC) can achieve exciting data compression and noise-resiliency performances for wireless image transmission tasks, especially in environments with low channel signal-to-noises (SNRs). However, existing DeepJSCC-based semantic communication frameworks still cannot achieve adaptive code rates for different channel SNRs and image contents, which reduces its flexibility and bandwidth efficiency. In this paper, we propose a predictive and adaptive deep coding (PADC) framework for realizing flexible code rate optimization with a given target transmission quality requirement. PADC is realized by a variable code length enabled DeepJSCC (DeepJSCC-V) model for realizing flexible code length adjustment, an Oracle Network (OraNet) model for predicting peak-signal-to-noise (PSNR) value for an image transmission task according to its contents, channel signal to noise ratio (SNR) and the compression ratio (CR) value, and a CR optimizer aims at finding the minimal data-level or instance-level CR with a PSNR quality constraint. By using the above three modules, PADC can transmit the image data with minimal CR, which greatly increases bandwidth efficiency. Simulation results demonstrate that the proposed DeepJSCC-V model can achieve similar PSNR performances compared with the state-of-the-art Attention-based DeepJSCC (ADJSCC) model, and the proposed OraNet model is able to predict high-quality PSNR values with an average error lower than 0.5dB. Results also demonstrate that the proposed PADC can use nearly minimal bandwidth consumption for wireless image transmission tasks with different channel SNR and image contents, at the same time guaranteeing the PSNR constraint for each image data.
AbstractList	Semantic communication is a newly emerged communication paradigm that exploits deep learning (DL) models to realize communication processes like source coding and channel coding. Recent advances have demonstrated that DL-based joint source-channel coding (DeepJSCC) can achieve exciting data compression and noise-resiliency performances for wireless image transmission tasks, especially in environments with low channel signal-to-noises (SNRs). However, existing DeepJSCC-based semantic communication frameworks still cannot achieve adaptive code rates for different channel SNRs and image contents, which reduces its flexibility and bandwidth efficiency. In this paper, we propose a predictive and adaptive deep coding (PADC) framework for realizing flexible code rate optimization with a given target transmission quality requirement. PADC is realized by a variable code length enabled DeepJSCC (DeepJSCC-V) model for realizing flexible code length adjustment, an Oracle Network (OraNet) model for predicting peak-signal-to-noise (PSNR) value for an image transmission task according to its contents, channel signal to noise ratio (SNR) and the compression ratio (CR) value, and a CR optimizer aims at finding the minimal data-level or instance-level CR with a PSNR quality constraint. By using the above three modules, PADC can transmit the image data with minimal CR, which greatly increases bandwidth efficiency. Simulation results demonstrate that the proposed DeepJSCC-V model can achieve similar PSNR performances compared with the state-of-the-art Attention-based DeepJSCC (ADJSCC) model, and the proposed OraNet model is able to predict high-quality PSNR values with an average error lower than 0.5dB. Results also demonstrate that the proposed PADC can use nearly minimal bandwidth consumption for wireless image transmission tasks with different channel SNR and image contents, at the same time guaranteeing the PSNR constraint for each image data.
Author	Wang, Ning Shao, Hua Leung, Victor C. M. Zhang, Haijun Zhang, Wenyu Ma, Hui
Author_xml	– sequence: 1 givenname: Wenyu orcidid: 0000-0001-5488-2158 surname: Zhang fullname: Zhang, Wenyu organization: Institute of Artificial Intelligence, School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China – sequence: 2 givenname: Haijun orcidid: 0000-0002-0236-6482 surname: Zhang fullname: Zhang, Haijun organization: Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China – sequence: 3 givenname: Hui orcidid: 0000-0002-2851-0091 surname: Ma fullname: Ma, Hui organization: Institute of Artificial Intelligence, School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China – sequence: 4 givenname: Hua orcidid: 0000-0002-1275-2280 surname: Shao fullname: Shao, Hua organization: Institute of Artificial Intelligence, School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China – sequence: 5 givenname: Ning orcidid: 0000-0001-9403-3417 surname: Wang fullname: Wang, Ning organization: School of Information Engineering, Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis, Zhengzhou University, Henan, China – sequence: 6 givenname: Victor C. M. orcidid: 0000-0003-3529-2640 surname: Leung fullname: Leung, Victor C. M. organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
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SubjectTerms	Bandwidths Codes Communication Compression ratio Data compression Data models Encoding Image coding Image communication Image compression Image transmission joint source channel coding Keywords: Adaptive code length Noise Noise prediction Optimization quality prediction semantic communication Semantics Signal to noise ratio Wireless communication wireless image transmission
Title	Predictive and Adaptive Deep Coding for Wireless Image Transmission in Semantic Communication
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