We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a non-trainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the "cliff effect", and it provides a graceful performance degradation 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 learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values. Joint source-channel coding, deep neural networks, image communications. E. Bourtsoulatze is with the Communications and layer; hence, the name deep JSCC. Two channel models, the additive white Gaussian noise (AWGN) channel and the slow Rayleigh fading channel, are considered in this work due to their widespread adoption in representing realistic channel conditions. The proposed solution is readily extendable to other channel models, as long as they can be represented as a nontrainable NN layer with a differentiable transfer function. DL-based methods, and, particularly, autoencoders, have recently shown remarkable results in image compression, achieving or even surpassing the performance of state-of-the-art March 19, 2019 DRAFT lossy compression algorithms [6]-[8]. The advantage of DL-based methods for lossy compression versus conventional compression algorithms lies in their ability to extract complex features from the training data thanks to their deep architecture, and the fact that their model parameters can be trained efficiently on large datasets through backpropagation. While common compression algorithms, such as JPEG, apply the same processing pipeline to all types of images (e.g., DCT transform, quantization and entropy coding in JPEG), the DL-based image compression algorithms learn the statistical characteristics from a large training dataset, and optimize the compression algorithm accordingly, without explicitly specifying a transform or a code. At the same time, the potential of DL has also been capitalized by researchers to design novel and efficient coding and modulation techniques in communications. In particular, the similarities between the autoencoder architecture and the digital communication systems have motivated significant research efforts in the direction of modelling end-to-end communication systems using the autoencoder architecture [9], [10]. Some examples of such designs include decoder design for existing channel codes [11], [12], blind channel equalization [13], learning physical layer signal representation for SISO [10] and MIMO [14] systems, OFDM systems [15], [16], JSCC of text messages [17] and JSCC for MNIST images for analog storage [18]. In this work, we leverage the recent success of DL methods in image compression and communication system design to propose a novel end-to-end JSCC algorithm for image transmission over wireless communication channels. We consider both time-invariant and fading AWGN channels, and compare the performance of our algorithm to the state-ofthe-art compression algorithms combined with capacity-achieving channel codes. We show through experiments that our solution achieves superior performance in low signal-to-noise ratio (SNR) regimes and for limited channel bandwidth, over a time-invariant AWGN channel. We also demonstrate that our approach is resilient to variations in channel conditions, and does not suffer from abrupt quality degradations, known as the "cliff effect" in digital communication systems: our algorithm exhibits graceful performance degradation when the channel conditions deteriorate. This latter property is particularly attractive when broadcasting the same image to multiple receivers with different channel qualities, or when transmitting to a single receiver over an unknown fading channel. Indeed, we show that March 19, 2019 DRAFT
In REF , deep learning was employed to perform joint source and channel coding, with raw signal values being directly mapped to complex-valued transmitted signals, and results confirmed the advantages of this design for image communication and its resilience to noise mismatch.
Deep Joint Source-Channel Coding for Wireless Image Transmission
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Mathematics Computer Science Engineering