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.
Deep Joint Source-Channel Coding of Images with Feedback / Kurka, D. B.; Gunduz, D.. - 2020-:(2020), pp. 5235-5239. (Intervento presentato al convegno 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 tenutosi a esp nel 2020) [10.1109/ICASSP40776.2020.9054216].
Deep Joint Source-Channel Coding of Images with Feedback
Gunduz D.
2020
Abstract
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.Pubblicazioni consigliate
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