Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding

09/26/2020
by   David Burth Kurka, et al.
0

We introduce 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 discrete 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 different 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 different strategies to divide the sources into a layered representation with negligible losses to the end-to-end performance when compared to a single transmission. Moreover, compared to state-of-the-art digital communication schemes, DeepJSCC-l performs well in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, and provides graceful degradation with channel SNR.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

03/15/2019

Successive Refinement of Images with Deep Joint Source-Channel Coding

We introduce deep learning based communication methods for successive re...
01/30/2021

SNR-adaptive deep joint source-channel coding for wireless image transmission

Considering the problem of joint source-channel coding (JSCC) for multi-...
11/25/2018

SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency Domain Sparsity

A hybrid digital-analog wireless image transmission scheme, called Spars...
10/09/2021

Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control

We present a novel adaptive deep joint source-channel coding (JSCC) sche...
11/30/2020

Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

Recent research on joint source channel coding (JSCC) for wireless commu...
07/07/2018

DeepSource: Point Source Detection using Deep Learning

Point source detection at low signal-to-noise is challenging for astrono...
12/01/2020

Neural network-based on-chip spectroscopy using a scalable plasmonic encoder

Conventional spectrometers are limited by trade-offs set by size, cost, ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.