Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Distortion-Tolerant Communications with Correlated Information

This dissertation is devoted to the development of distortion-tolerant communication techniques by exploiting the spatial and/or temporal correlation in a broad range of wireless communication systems under various system configurations. Signals observed in wireless communication systems are often correlated in the spatial and/or temporal domains, and the correlation can be used to facilitate system designs and to improve system performance. First, the optimum node density, i.e., the optimum number of nodes in a unit area, is identified by utilizing the spatial data correlation in the one- and two-dimensional wireless sensor networks (WSNs), under the constraint of fixed power per unit area. The WSNs distortion is quantized as the mean square error between the original and the reconstructed signals. Then we extend the analysis into WSNs with spatial-temporally correlated data. The optimum sampling in the space and time domains is derived. The analytical optimum results can provide insights and guidelines on the design of practical WSNs. Second, distributed source coding schemes are developed by exploiting the data correlation in a wireless network with spatially distributed sources. A new symmetric distributed joint source-channel coding scheme (DJSCC) is proposed by utilizing the spatial source correlation. Then the DJSCC code is applied to spatial-temporally correlated sources. The temporal correlated data is modeled as the Markov chain. Correspondingly, two decoding algorithms are proposed. The first multi-codeword message passing algorithm (MCMP) is designed for spatially correlated memoryless sources. In the second algorithm, a hidden Markov decoding process is added to the MCMP decoder to effectively exploit the data correlation in both the space and time domains. Third, we develop distortion-tolerant high mobility wireless communication systems by considering correlated channel state information (CSI) in the time domain, and study the optimum designs with imperfect CSI. The pilot-assisted channel estimation mean square error is expressed as a closed-form expression of various system parameters through asymptotic analysis. Based on the statistical properties of the channel estimation error, we quantify the impacts of imperfect CSI on system performance by developing the analytical symbol error rate and a spectral efficiency lower bound of the communication system

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme

Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes

Optimization and Applications of Modern Wireless Networks and Symmetry

Due to the future demands of wireless communications, this book focuses on channel coding, multi-access, network protocol, and the related techniques for IoT/5G. Channel coding is widely used to enhance reliability and spectral efficiency. In particular, low-density parity check (LDPC) codes and polar codes are optimized for next wireless standard. Moreover, advanced network protocol is developed to improve wireless throughput. This invokes a great deal of attention on modern communications

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Compression pour la communication interactive de contenus visuels

Interactive images and videos have received increasing attention due to the interesting features they provide. With these contents, users can navigate within the content and explore the scene from the viewpoint they desire. The characteristics of these media make their compression very challenging. On the one hand, the data is captured in high resolution (very large) to experience a real sense of immersion. On the other hand, the user requests a small portion of the content during navigation. This requires two characteristics: efficient compression of data by exploiting redundancies within the content (to lower the storage cost), and random access ability to extract part of the compressed stream requested by the user (to lower the transmission rate). Classical compression schemes can not handle random accessibility because they use a fixed pre-defined order of sources to capture redundancies.The purpose of this thesis is to provide new tools for interactive compression schemes of images. For that, as the first contribution, we propose an evaluation framework by which we can compare different image/video interactive compression schemes. Moreover, former theoretical studies show that random accessibility can be achieved using incremental codes with the same transmission cost as non-interactive schemes and with reasonable storage overhead. Our second contribution is to build a generic coding scheme that can deal with various interactive media. Using this generic coder, we then propose compression tools for 360-degree images and 3D model texture maps with random access ability to extract the requested part. We also propose new representations for these modalities. Finally, we study the effect of model selection on the compression rates of these interactive coders.Les images et vidéos interactives ont récemment vu croître leur popularité. En effet, avec ce type de contenu, les utilisateurs peuvent naviguer dans la scène et changer librement de point de vue. Les caractéristiques de ces supports posent de nouveaux défis pour la compression. D'une part, les données sont capturées en très haute résolution pour obtenir un réel sentiment d'immersion. D'autre part, seule une petite partie du contenu est visualisée par l'utilisateur lors de sa navigation. Cela induit deux caractéristiques : une compression efficace des données en exploitant les redondances au sein du contenu (pour réduire les coûts de stockage) et une compression avec accès aléatoire pour extraire la partie du flux compressé demandée par l'utilisateur (pour réduire le débit de transmission). Les schémas classiques de compression ne peuvent gérer de manière optimale l’accès aléatoire, car ils utilisent un ordre de traitement des données fixe et prédéfini qui ne peut s'adapter à la navigation de l'utilisateur.Le but de cette thèse est de fournir de nouveaux outils pour les schémas interactifs de compression d’images. Pour cela, comme première contribution, nous proposons un cadre d’évaluation permettant de comparer différents schémas interactifs de compression d'image / vidéo. En outre, des études théoriques antérieures ont montré que l’accès aléatoire peut être obtenu à l’aide de codes incrémentaux présentant le même coût de transmission que les schémas non interactifs au prix d'une faible augmentation du coût de stockage. Notre deuxième contribution consiste à créer un schéma de codage générique pouvant s'appliquer à divers supports interactifs. À l'aide de ce codeur générique, nous proposons ensuite des outils de compression pour deux modalités d'images interactives : les images omnidirectionnelles (360 degrés) et les cartes de texture de modèle 3D. Nous proposons également de nouvelles représentations de ces modalités. Enfin, nous étudions l’effet de la sélection du modèle sur les taux de compression de ces codeurs interactifs