DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding
- URL: http://arxiv.org/abs/2111.13042v1
- Date: Thu, 25 Nov 2021 11:59:17 GMT
- Title: DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding
- Authors: Tze-Yang Tung, David Burth Kurka, Mikolaj Jankowski, Deniz G\"und\"uz
- Abstract summary: DeepJSCC-Q is an end-to-end optimized joint source-channel coding scheme for wireless image transmission.
It preserves the graceful degradation of image quality observed in prior work when channel conditions worsen.
- Score: 5.046831208137847
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent works have shown that the task of wireless transmission of images can
be learned with the use of machine learning techniques. Very promising results
in end-to-end image quality, superior to popular digital schemes that utilize
source and channel coding separation, have been demonstrated through the
training of an autoencoder, with a non-trainable channel layer in the middle.
However, these methods assume that any complex value can be transmitted over
the channel, which can prevent the application of the algorithm in scenarios
where the hardware or protocol can only admit certain sets of channel inputs,
such as the use of a digital constellation. Herein, we propose DeepJSCC-Q, an
end-to-end optimized joint source-channel coding scheme for wireless image
transmission, which is able to operate with a fixed channel input alphabet. We
show that DeepJSCC-Q can achieve similar performance to models that use
continuous-valued channel input. Importantly, it preserves the graceful
degradation of image quality observed in prior work when channel conditions
worsen, making DeepJSCC-Q much more attractive for deployment in practical
systems.
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