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.

Flexible call admission control with preemption in LTE networks

Abstract: This paper introduces a new call admission control (CAC) mechanism for Long Term Evolution (LTE) networks supporting multimedia services with different classes of traffic. Our CAC mechanism classifies calls into real time and non-real time users, then estimates the channel quality based upon the received signal strength (RSS) value, and finally identifies the call as either new call (NC) or handoff call (HC) request before performing admission control decision. We also use a simple preemption technique in order to allocate the resources to high priority bearer requests. We show through extensive simulation analysis that our CAC mechanism provides high number of accepted users with higher priorities while providing high system throughput.

Software defined networking (SDN) for reliable user connectivity in 5G networks

Abstract: Reliability was, and still is, a major concern for telecoms operators. Network outage can potentially produce high penalty in terms of revenue and user quality of experience. In this paper, we advocate that the data plane reliability can be easily improved when the Software Defined Networking (SDN) concept is incorporated into LTE/EPC networks. As a way to ensure user connectivity be available even in case of network equipment failures, we propose two recovery modes: (i) automatic tunnel re-establishment and (ii) on-demand tunnel re-establishment. Through simulations, we show that those mechanisms outperform the recovery mechanism previously proposed in 3GPP standards.

Distributed E2E QoS-Based Path Computation Algorithm over Multiple Inter-domain Routes

Abstract: Internet usages have changed with the emergence of value added services relying on a higher interactivity and needs for a better quality of experience (QoE). Telecommunication operators have to face a continuing growth of new types of Internet traffic (video, games, telepresence, etc.) imposing not only a more efficient utilization of their network infrastructure resources, but also the generation of new revenues to pursue investments and sustain the increasing demand. Such services generally cross multiple domains, but inter-domain routing protocols still have some limitations in terms of service assurance. For example, BGP's single route announce for a destination limits potential traffic engineering features (e.g. no quality of service price/efficiency optimisation, inter-domain shared route protection, inter-domain load balancing, etc.). In order to provision end-to-end inter-domain connections that obey to constraints such as bandwidth, delay, jitter, and packet loss for these services, an interesting approach is to compute end-to-end (e2e) paths over multiple inter-domain routes. This will allow establishing more efficiently the inter-domain connections with respect to requested QoS constraints and sharing these constraints (and associated revenues) among multiple operators to globally accept more demands in the system, while keep satisfying the customer QoE. To address these challenges, we propose an efficient distributed inter-domain algorithm that computes such constrained paths among a set of domains, exploring multiple inter-domain routes. We demonstrate that our algorithm not only increases success rate in delivering feasible paths, but also admits more connections and keeps a reasonable runtime.

Extending node protection concept of p-cycles for an efficient resource utilization in multicast traffic

Abstract: In this paper, we consider link-and-node failure recovery in dynamic multicast traffic in WDM networks. We extend the node protection concept of the p-cycle approach to achieve more efficient resource utilization. Then, we propose a novel algorithm that integrates our concept for the node protection, named node-and-link protecting p-cycle based algorithm (NPC). We also propose a second algorithm, named node-and-link protecting candidate p-cycle based algorithm (NPCC). This algorithm deploys our concept for node protection and relies on a candidate p-cycle set to speed up the computational time. We compare our proposed algorithms to the ESHN algorithm, which is reported to be the most efficient algorithm for protecting dynamic multicast sessions. Extensive simulations show that the NPC algorithm achieves the lowest blocking probability, but has the highest computational time among the NPCC and ESHN algorithms. The NPCC algorithm outperforms the ESHN algorithm in terms of resource utilization efficiency and computational time.

On the legacy amplifier limitation in flexgrid optical networks

Abstract: Flexgrid technology is an interesting solution to improve network capacity. However, for a given spectral band, it gives rise to the increase of the number of channels, requiring more amplification power in respect with the conventional fixed grid technology. In this work, we demonstrate that re-engineering the link margins allows supporting this increase while keeping in use legacy amplifiers.

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.