Joint Source-Channel Coding of Images with (not very) Deep Learning
David Burth Kurka, Denız Gündüz · Repository for Publications and Research Data (ETH Zurich) · 2020
Almost all wireless communication systems today are designed based on essentially the same digital approach, that separately optimizes the compression and channel coding stages.Using machine learning techniques, we investigate whether end-toend transmission can be learned from scratch, thus using joint source-channel coding (JSCC) rather than the separation approach.This paper reviews and advances recent developments on our proposed technique, deep-JSCC, an autoencoder-based solution for generating robust and compact codes directly from images pixels, being comparable or even superior in performance to state-of-the-art (SoA) separation-based schemes (BPG+LDPC).Additionally, we show that deep-JSCC can be expanded to exploit a series of important features, such as graceful degradation, versatility to different channels and domains, variable transmission rate through successive refinement, and its capability to exploit channel output feedback.