'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
We propose a novel joint source and channel coding (JSCC)
scheme for wireless image transmission that departs from
the conventional use of explicit source and channel codes for
compression and error correction, and directly maps the image pixel values to the complex-valued channel input signal.
Our encoder-decoder pair form an autoencoder with a nontrainable layer in the middle, which represents the noisy communication channel. Our results show that the proposed deep
JSCC scheme outperforms separation-based digital transmission at low signal-to-noise ratio (SNR) and low channel bandwidth regimes in the presence of additive white Gaussian
noise (AWGN). More strikingly, deep JSCC does not suffer
from the “cliff effect” as the channel SNR varies with respect
to the SNR value assumed during training. In the case of a
slow Rayleigh fading channel, deep JSCC can learn to communicate without explicit pilot signals or channel estimation,
and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values
