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.

Comparaison de l'obs conventionnel et de l'obs a label

Abstract: Dans cet article, nous montrons, a l'aide de simulations, les differents comportements de l'OBS ("Optical Burst Switching") conventionnel et de l'OBS a label vis-a-vis de la resolution de contentions spectro-temporelles. Nous montrons que le temps d' "offset" et les contentions de BCP ("Burst Control Packet") sont les principaux inconvenients de l'OBS conventionnel entrainant des performances generales moindres que l'OBS a label.

Power fairness for multicast traffic in optical networks with adaptive light splitters

Abstract: To perform data multicasting in the optical layer, optical nodes must be equipped with light splitters. Light splitters can split one light wave to more than one node output. A lot of work had been done in order to enhance the structure of the light splitter in a way to reduce its cost and enhance its performance in terms of the power loss resulted of multiple splitting. To guarantee the fairness of power received by different members of a same multicast group, the use of adaptive light splitters is required. Adaptive light splitters allow splitting an incoming light signal into two or more node output with the ability of varying the individual power of each output signal. This paper studies the benefits of using adaptive splitters on the value of the power received by each of the multicast group members in a way to assure fairness among all members.

Tree Reconfiguration without Lightpath Interruption in Wavelength Division Multiplexing Optical Networks with Limited Resources

Abstract: Today, operators use reconfiguration to improve the performance of connection oriented networks. In our previous work, we studied tree reconfiguration without data flow interruption in wavelength division multiplexing optical networks. In our previous approach, the available resources of the network (wavelengths) are not considered as limited. In this work, we study tree reconfiguration in a network which has a limited number of wavelengths per link. This paper proposes an algorithm called TRwRC (tree reconfiguration with resources constraint). TRwRC reconfigures a tree without data flow interruption in a network with a limited number of wavelengths.

Performance evaluation of legacy QCN for multicast and multiple unicast traffic transmission

Abstract: A multicast congestion control scheme is an interesting feature to control group communication applications such as teleconferencing tools and information dissemination services. This paper addresses a comparison between multiple unicast and multicast traffic congestion control for Carrier Ethernet. In this work, we proposed to study the quantized congestion notification QCN, which is a layer 2 congestion control scheme, in the case of multicast traffic and multiple unicast traffic. Indeed, the QCN has recently been standardized as the IEEE 802.1Qau Ethernet Congestion Notification standard. This scheme is evaluated through simulation experiments, which are implemented by the OMNeT++ framework. This paper evaluates the reaction point start time congestion detection, feedback rate, loss rate, stability, fairness and scalability performance of the QCN for multicast traffic transmission and multiple unicast traffic transmission. This paper also draws a parallel between QCN for multicast traffic transmission and that for multiple unicast traffic transmission. Despite the benefit of integrating the multicast traffic, results show that performance could degrade when the network scales up. The evaluation results also show that it is probable that the feedback implosion problem caused by the bottlenecks could be solved if we choose to set the queue parameter Qeq threshold value at a high value, 75% of the queue capacity for instance. Copyright © 2016 John Wiley & Sons, Ltd.

Neural network approach for component carrier selection in 4G/5G networks

Abstract: In order to ensure high transmission rate to a wide range of voice, video and data services, carrier aggregation (CA) has been introduced in the Long Term Evolution Advanced (LTE-A). Moreover, CA is one of the principal enabling technologies for the 5G. Indeed, 4G and 5G can aggregate up to five component carriers (CC), simultaneously, to support a higher bandwidth. In this context, CC selection method is necessary. In this paper, we propose a new CC selection method in order to maximize the global system throughput. Neural network approach is introduced to select the best couple user-CC via a utility function. Simulation results prove that the proposed method outperforms in terms of system throughput, fairness and packet loss rate.

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.