期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 20, 期 12, 页码 8081-8095出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2021.3090048
关键词
Image transmission; joint source-channel coding; multiple description coding; successive refinement; wireless communication
资金
- European Research Council (ERC) Starting Grant BEACON [677854]
- U.K. Engineering and Physical Sciences Research Council (EPSRC) [EP/T023600/1]
- EPSRC [EP/T023600/1] Funding Source: UKRI
The study introduces deep learning based communication methods for adaptive-bandwidth transmission of images, proposing DeepJSCC-l as an innovative solution utilizing convolutional autoencoders. Experimental results demonstrate comparable performance of DeepJSCC-l with state of the art digital progressive transmission schemes in low signal-to-noise ratio and small bandwidth regimes.
We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels. We consider the scenario in which images are transmitted progressively in layers over time or frequency, and such layers can be aggregated by receivers in order to increase the quality of their reconstructions. We investigate two scenarios, one in which the layers are sent sequentially, and incrementally contribute to the refinement of a reconstruction, and another in which the layers are independent and can be retrieved in any order. Those scenarios correspond to the well known problems of successive refinement and multiple descriptions, respectively, in the context of joint source-channel coding (JSCC). We propose DeepJSCC-l, an innovative solution that uses convolutional autoencoders, and present three architectures with different complexity trade-offs. To the best of our knowledge, this is the first practical multiple-description JSCC scheme developed and tested for practical information sources and channels. Numerical results show that DeepJSCC-l can learn to transmit the source progressively with negligible losses in the end-to-end performance compared with a single transmission. Moreover, DeepJSCC-l has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional advantage of graceful degradation with channel SNR.
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