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

We propose 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 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 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 to transmit the source progressively with negligible losses in the end-to-end performance compared with a single transmission. Moreover, DeepJSCC-l has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional advantage of graceful degradation with channel SNR

Deep Joint Source-Channel Coding for Wireless Image Transmission

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

DeepJSCC-f: deep joint source-channel coding of images with feedback

We consider wireless transmission of images in the presence of channel output feedback. From a Shannon theoretic perspective feedback does not improve the asymptotic end-to-end performance, and separate source coding followed by capacity-achieving channel coding, which ignores the feedback signal, achieves the optimal performance. It is well known that separation is not optimal in the practical finite blocklength regime; however, there are no known practical joint source-channel coding (JSCC) schemes that can exploit the feedback signal and surpass the performance of separation-based schemes. Inspired by the recent success of deep learning methods for JSCC, we investigate how noiseless or noisy channel output feedback can be incorporated into the transmission system to improve the reconstruction quality at the receiver. We introduce an autoencoder-based JSCC scheme, which we call DeepJSCC-f, that exploits the channel output feedback, and provides considerable improvements in terms of the end-to-end reconstruction quality for fixed-length transmission, or in terms of the average delay for variable-length transmission. To the best of our knowledge, this is the first practical JSCC scheme that can fully exploit channel output feedback, demonstrating yet another setting in which modern machine learning techniques can enable the design of new and efficient communication methods that surpass the performance of traditional structured coding-based designs

Incidence of competitors and landscape structure as predictors of woodland-dependent birds

Globally, modification of landscapes for agriculture has had a strong influence on the distribution and abundance of biota. In particular, woodland-dependent birds are under threat across agricultural landscapes in Britain, North America and Australia, with their decline and extirpation attributed to the loss and fragmentation of habitat. Other native species have become over-abundant in response to anthropogenic landscape change and have strong interactive effects on avian assemblage structure. In eastern Australia, the hyper-aggressive noisy miner (Manorina melanocephala) often dominates woodlands in agricultural landscapes through interspecific competition, resulting in declines of species richness of woodland-dependent birds. We aimed to determine the relative influence and importance of interspecific competition, in situ habitat structure and landscape structure for woodland-dependent bird species at the landscape level. We recorded species-specific landscape incidence of woodland-dependent birds in 24 agricultural-woodland mosaics (25 km(2)) in southern Queensland, Australia. We selected extensively cleared landscapes (10-23 % woodland cover) where fragmentation effects are expected to be greatest. We applied generalised linear models and hierarchical partitioning to quantify the relative importance of the landscape-level incidence of the noisy miner, mistletoe abundance, shrub cover, woodland extent, woodland subdivision and land-use intensity for the incidence of 46 species of woodland birds at the landscape-scale. The landscape-level incidence of the noisy miner was the most important explanatory variable across the assemblage. Both in situ habitat structure and landscape structure were of secondary importance to interspecific aggression, although previous research suggests that the increasing incidence of the noisy miner in fragmented agricultural landscapes is itself a consequence of anthropogenic changes to landscape structure. Species' responses to fragmentation varied from positive to negative, but complex habitat structure had a consistently positive effect, suggesting in situ restoration of degraded habitats could be a conservation priority. Landscape wide conservation of woodland-dependent bird populations in agricultural landscapes may be more effective if direct management of noisy miner populations is employed, given the strong negative influence of this species on the incidence of woodland-dependent birds among landscapes