DEEP JOINT SOURCE-CHANNEL CODING OF IMAGES WITH FEEDBACK

被引:0
|
作者
Kurka, David Burth [1 ]
Gunduz, Deniz [1 ]
机构
[1] Imperial Coll London, Dept Elect & Elect Engn, London, England
来源
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2020年
基金
欧洲研究理事会;
关键词
Deep neural networks; Feedback; Joint source-channel coding; Wireless image transmission; VECTOR QUANTIZATION; RATE-DISTORTION; COMMUNICATION;
D O I
10.1109/icassp40776.2020.9054216
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
We consider wireless transmission of images in the presence of channel output feedback, by introducing an autoencoder-based deep joint source-channel coding (JSCC) scheme. We achieve impressive results in terms of the end-to-end reconstruction quality for fixed length transmission, and in terms of the average delay for variable length transmission. To the best of our knowledge, this is the first practical JSCC scheme that can fully exploit channel output feedback, demonstrating yet another setting in which modern machine learning techniques can enable the design of new and efficient communication methods that surpass the performance of traditional structured coding schemes.
引用
收藏
页码:5235 / 5239
页数:5
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