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We consider wireless transmission of images in the presence of channel outputfeedback. From a Shannon theoretic perspective feedback does not improve theasymptotic end-to-end performance, and separate source coding followed bycapacity-achieving channel coding, which ignores the feedback signal, achievesthe optimal performance. It is well known that separation is not optimal in thepractical finite blocklength regime; however, there are no known practicaljoint source-channel coding (JSCC) schemes that can exploit the feedback signaland surpass the performance of separation-based schemes. Inspired by the recentsuccess of deep learning methods for JSCC, we investigate how noiseless ornoisy channel output feedback can be incorporated into the transmission systemto improve the reconstruction quality at the receiver. We introduce anautoencoder-based JSCC scheme, which we call DeepJSCC-f, that exploits thechannel output feedback, and provides considerable improvements in terms of theend-to-end reconstruction quality for fixed-length transmission, or in terms ofthe average delay for variable-length transmission. To the best of ourknowledge, this is the first practical JSCC scheme that can fully exploitchannel output feedback, demonstrating yet another setting in which modernmachine learning techniques can enable the design of new and efficientcommunication methods that surpass the performance of traditional structuredcoding-based designs.

DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback