A Novel Deep Learning Architecture for Wireless Image Transmission

Author:

Wang Sixian1,Dai Jincheng1,Yao Shengshi1,Niu Kai1,Zhang Ping2

Affiliation:

1. Ministry of Education,Key Laboratory of Universal Wireless Communications

2. Beijing University of Posts and Telecommunications,State Key Laboratory of Networking and Switching Technology,Beijing,China,100876

Funder

National Natural Science Foundation of China

Publisher

IEEE

Reference22 articles.

1. A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges

2. The turbo principle: Tutorial introduction and state of the art;hagenauer;Proc International Symposium on Turbo Codes and Related Topics,0

3. Bina-ryconnect: Training deep neural networks with binary weights during propagations;courbariaux;ArXiv Preprint,2015

4. Use of residual redundancy in the design of joint source/channel coders

5. Joint source and channel coding for image trans-mission over lossy packet networks;tescher;Spies International Symposium on Optical Science Engineering and Instrumentation p SPIE,0

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Channel-Blind Joint Source–Channel Coding for Wireless Image Transmission;Sensors;2024-06-20

2. Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding;IEEE Journal on Selected Areas in Communications;2023-08

3. Distributed Image Transmission Using Deep Joint Source-Channel Coding;ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2022-05-23

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