Power Aware Routing in Mobile Ad Hoc Networks

To support bursty traffic on the Internet (and especially WWW) efficiently, optical burst switching (OBS) is proposed as a way to streamline both protocols and hardware in building the future gener...

Optical burst switching (OBS) - a new paradigm for an optical Internet

Computer communications

In the fifth-generation (5G) mobile communication system, various service requirements of different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous network (HetNet) has been studied in recent years. Compared with homogeneous networks, HetNets can increase the opportunity in the spatial resource reuse and improve users’ quality of service by developing small cells into the coverage of macrocells. Since there is mutual interference among different users and the limited spectrum resource in HetNets, however, efficient resource allocation (RA) algorithms are vitally important to reduce the mutual interference and achieve spectrum sharing. In this article, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNets. Second, RA models are discussed. Then, we present a classification to analyze current RA algorithms for the existing works. Finally, some challenging issues and future research trends are discussed. Accordingly, we provide two potential structures for 6G communications to solve the RA problems of the next-generation HetNets, such as a learning-based RA structure and a control-based RA structure. The goal of this article is to provide important information on HetNets, which could be used to guide the development of more efficient techniques in this research area.

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A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Cette presentation va decrire les differents mecanismes de controle de congestion mis en oeuvre au sein du reseau ATM. Nous allons d'abord decrire en quoi ces mecanismes de congestion sont indispensables au bon fonctionnement du reseau : le multiplexage statistique provoque des congestions. Puis apres avoir montre la specificite de ces mecanismes au sein des reseaux a haut debit, nous proposons quelques elements de classification des differents mecanismes de controle de congestion, notamment le temps de reaction. Nous introduisons la notion de contrat de trafic : le descripteur de trafic contient les parametres qui definissent le trafic qui sera negocie et puis controle par nos mecanismes. Deux mecanismes de controle de congestion preventifs sont decrits : le controle de l'etablissement des connexions et le controle de conformite du trafic. Le premier intervient lors de la demande d'etablissement des connexions afin de ne pas surcharger les ressources du reseau. Le second controle la conformite du trafic soumis par les usagers ou les autres reseaux. Puis deux mecanismes de controle de congestion reactifs sont presentes : le mecanisme de rejet selectif de cellules et celui de retrocontrole. Le premier choisit les cellules a detruire pour traiter la congestion, le second permet de reduire le debit soumis par l'application afin de ne pas amplifier (et si possible eviter) la congestion. Nous concluons par quelques considerations sur d'autres mecanismes de controle de congestion.

https://hal.archives-ouvertes.fr/hal-01501466/document

Bernard Cousin

Papers

Les mécanismes de contrôle de congestion dans ATM