Bernard Cousin

Bio: Bernard Cousin is an academic researcher from University of Rennes. The author has contributed to research in topics: Multicast & Source-specific multicast. The author has an hindex of 20, co-authored 136 publications receiving 1239 citations. Previous affiliations of Bernard Cousin include Institut de Recherche en Informatique et Systèmes Aléatoires & University of Rennes 1.

Equal Cost Multiple Path Energy-Aware Routing in Carrier-Ethernet Networks with Bundled Links

Abstract: The reduction of operational expenditure has become a major concern for telecommunication operators and Internet service providers. In this paper, we propose an energy aware routing (EAR) in Carrier Ethernet networks operating with Shortest Path Bridging (SPB) protocol with equal cost multi-path (ECMP). Since traffic load has no influence on power consumption of Carrier Ethernet network elements, the conventional solution to reduce power consumption is to find the maximal set of network elements that can be turned off on so that the network performance is not deteriorated. To tackle this optimization problem, we propose an exact method based on Mixed Integer Linear Programming (MILP) formulation, called SPB energy-aware routing (SPB-EAR). Since SPB-EAR is proved to be NP-hard, we present two heuristics algorithm suitable for large-sized networks, called Green SPB (G-SPB) and Fast Greedy SPB (FG-SPB). In this work, we consider that a connection between two nodes is represented by bundled link consisting of multiple cables. Experimentations on four realistic network topologies show that G-SPB and FG-SPB can save almost as much power consumption as SPB-EAR.

Cross-Layer Multi-User Selection in 5G Heterogeneous Networks Based on Hybrid Beamforming Optimization for Millimeter-Wave

Abstract: Lack of coordination between network layers limits the performance of most proposed solution for new challenges posed by wireless networks. To overcome such limitations, cross-layer physical and medium access (PHY-MAC) design for multi-input-multi-output orthogonal frequency division multiple access system in heterogeneous networks (HetNETs) is proposed. In this paper, we formulate an optimization problem for hybrid beamforming, in a multi-user HetNET scenario aiming to maximize the total system throughput. Furthermore, analog beamforming is selected from a codebook containing a limited number of candidates for steering vectors. The proposed problem is non-convex and hard to solve. Thus it is relaxed by transforming it into a subtraction form of two convex funcions. Afterward we apply a group of well-known metaheuristic algorithms to calculate the normalized hybrid beamforming vectors. The optimal solution is obtained using an exhaustive search (ES) algorithm that provides an ideal solution, but with high complexity. In addition, zero-forcing-based approach (ZFA), matched filter (MF), and QR-based approach (QR) are applied to get quick sub-optimal solutions. Hence, we analyze the performance of our systems using the throughput metric. The simulation results show that QR algorithm outperforms ZFA and MF in low and middle signal-tonoise ratio (SNR) regime, while ZFA outperforms QR and MF at higher SNRs. Moreover, QR is close to the optimal solution ES.

The ASSET Architecture: Integrating Media Applications and Broadcasting Products in a Unified Model and Framework

Abstract: To design and develop an Architectural Solution for Services Enhancing digital Television is the goal of the ASSET project. This European funded project gathers eight partners (Compaq-HP Group, THOMSON, Dalet a.n.n, INESC Porto, INRIA, Institut fuer Rundfunktechnik, Front Porch Digital International, SHS Multimedia) to create a universal and unified system architecture that shall greatly improve interoperability in broadcasting environments. The implementation of this solution will indeed make integration of different proprietary systems and media applications much easier. This paper outlines the ASSET architecture, describes some key-concepts of the ASSET framework and presents the prototype currently under development.

Light-hierarchies: Optimal multicast routes under optical constraints

Abstract: Multicast routing in all optical WDM networks where the light splitting capacity of some optical switches is limited is an important problem. The computation problem of the minimum cost multicast routes under optical constraints is NP-difficult. To solve the optimal multicast routing problem under physical constraints, we propose a new routing structure called 'light-hierarchy''. In a light-hierarchy, the multicast route can traverse the same optical switch several times using the same wavelength. This new routing structure may improve both the cost of the optical structures and the throughput on WDM multicast networks.

The ASSET Architecture - Integrating Media Applications and Products through a Unified API

Abstract: Applications and products currently available for the broadcasting market are vertically integrated or proprietary. They are based on components requiring specific and costly development to interoperate and do rely typically on a single manufacturer or system integrator. Hence they are not fully compliant with broadcasters' requirements. ASSET is a European funded project whose main goal is to overcome the limitations of custom specific implementations of a digital system for TV content creation. These limitations are generally due to the misfit of interfaces between software layers, proprietary APIs of equipment from different vendors and the lack of a generalised middleware for multimedia content management with openly defined interfaces. Besides presenting the ASSET proposed architecture and concepts, this paper describes the prototype under development to test and demonstrate the project proposals.

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

Abstract: 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...

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

Abstract: 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.

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

Abstract: 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.