PhD defense of Cesar Augusto VARGAS ANAMURO on June 25th, 2020

The defense of PhD thesis of Cesar Augusto VARGAS ANAMURO will be held on June 25th, 2020 at 14:00 with a conf call (due to the Covid-19 pandemy situation). The thesis is entitled “Study of terminal-assisted relaying for machine-type devices in 5G networks”. It was made in the framework of a CIFRE thesis with Orange Labs (Meylan).

You can contact the team leader to have the connection references.

PhD defense of Romuald CORBEL on December 4th, 2019

The defense of PhD thesis of Romuald Corbel will be held on December 4that  13:30 at IMT Atlantique, Rennes Campus , Petit Amphi. The thesis is entitled “Transport protocol evolution from a fairness point of view”. It was made in the framework of a CIFRE thesis with Orange Labs (Lannion).

PhD defense of Ali EL AMINE on November 12th, 2019

The defense of PhD thesis of Ali El Amine will be held on November 12th at  14:00 at IMT Atlantique, Rennes Campus , Petit Amphi. The thesis is entitled “Radio resource allocation in 5G cellular networks powered by the smart grid and renewable energies”.

The heated 5G network deployment race had begun between competitors to outperform one another and be the most innovative followed by a rapid progress towards standardization.

Unlike previous generations, 5G envisions to support extremely wide diversity of services and applications with different requirements in terms of reliability, network availability, data rate and latency with high energy efficiency. This thesis focuses on studying the role of energy and its behavior while designing and operating wireless cellular networks. We consider different and complementary approaches and parameters, including energy efficiency techniques (i.e., radio resource management and sleep schemes), renewable energy sources, smart grid and tools from machine learning, to bring down the energy consumption of these complex networks while guaranteeing a certain quality of service adapted to 5G use cases.

In the past decade, a lot of research efforts have been devoted to design energy efficient cellular networks not only to reduce the energy consumption, but also to limit the carbon footprint induced by these systems. Focusing on renewable energy to bring down the Operational Expenditure (OpEx) costs for telecom operators while respecting the environmental regulations opened opportunities for new business models. However, it is not trivial to design and operate such networks given the complex nature of these networks. In addition to radio resource management, green cellular networks require the optimization of using renewable energy that entails high management complexity due to its erratic and intermittent nature. Moreover, considering the energy storage element (i.e., battery), a critical component in renewable energy-equipped systems, and the Smart Grid environment provide additional dimensions to the problem and open new research challenges.

In this work, we start by a review of the literature in order to identify the different research directions in energy efficiency green wireless networks. Following the extensive research under these areas, we highlight several aspects in which the problematic of green cellular networks needs more exploration.

Consequently, we first study the effect of equipping base stations with renewable energy sources. Due to the high capital costs of these systems and the infeasibility to equip renewable energy systems to all base station sites, we study the percentage of sites to be powered with hybrid energy supplies (renewable energy and Smart Grid). In particular, we focus on the impact of equipping sites with renewables on the operational cost and the performance of a cellular network to decide how much to invest in renewable energy, i.e., number of sites equipped with renewable energy sources, sizing of renewable energy and battery capacity. Our study for instance shows that it is enough to equip 30% of sites with renewables in order to realize an operational cost gain of 60%.

Then, we evaluate the contribution of each service the network is providing and their effect on the network energy consumption. We consider Key Performance Indicators (KPIs) putting forward each service contribution to energy consumption. Using these KPIs, we propose some energy management strategies, leading to performance amelioration and energy savings up to 11:5% points compared to other benchmark algorithms, under renewable energy and smart grid environment.

Focusing on the storage element (i.e., battery) that requires expensive investment cost both in terms of Capital Expenditure (CapEx) and OpEx, we include important constraints on the battery that is prone to irreversible aging mechanisms to expand its life span. Then, we propose several energy management algorithms that aim at saving energy while respecting the battery constraints. Our results show a gain of 20% in terms of electric bill reduction compared to an existing algorithm, and 35% battery life time enhancement.

Recently, artificial intelligence has received significant attention as a highly effective alternative to conventional methods. In particular, we take advantage of Reinforcement Learning (Q-learning) to orchestrate different levels of sleep modes to save energy given a user Quality of Service (QoS). By considering advanced sleep mode levels compliant with 5G requirements, we demonstrate the performance of these sleep schemes under the energy-delay-tradeoff problem.

PhD defense of Maha MDINI on September 20th, 2019

The defense of PhD thesis of Maha MDINI will be held on September 20 th  at 14:15 at IMT Atlantique, Rennes Campus, Petit Amphi. The thesis is entitled “Anomaly Detection and Root Cause Diagnosis in Cellular Networks”. It was made in the framework of a CIFRE thesis with Exfo (formely known as Astellia).

With the evolution of automation and artificial intelligence tools, mobile networks have become more and more machine reliant. Today, a large part of their management tasks runs in an autonomous way, without human intervention. The latest standards of the Third Generation Partnership Project (3GPP) aim at creating Self-Organizing Network (SON) where the processes of configuration, optimization and healing are fully automated. This work is about the healing process. This question have been studied by many researchers. They designed expert systems and applied Machine Learning (ML) algorithms in order to automate the healing process. However this question is still not fully addressed. A large part of the network troubleshooting still rely on human experts. For this reason, we have focused in this thesis on taking advantage of data analysis tools such as pattern recognition and statistical approaches to automate the troubleshooting task and carry it to a deeper level. The troubleshooting task is made up of three processes: detecting anomalies, analyzing their root causes and triggering adequate recovery actions. In this thesis, we focus on the two first objectives: anomaly detection and root cause diagnosis. The first objective is about detecting issues in the network automatically without including expert knowledge. To meet this objective, we have created an Anomaly Detection System (ADS) that learns autonomously from the network traffic and detects anomalies in real time in the flow of data. The algorithm we propose, Watchmen Anomaly Detection (WAD), is based on pattern recognition.  The second objective is automatic diagnosis of network issues. This project aims at identifying the root cause of issues without any prior knowledge about the network topology and services. To address this question, we have designed an algorithm, Automatic Root Cause Diagnosis (ARCD) that identifies the roots of network issues. ARCD is composed of two independent threads: inefficiency Major Contributor identification and Incompatibility detection. WAD and ARCD have been proven to be effective. However, many improvements of these algorithms are possible. This thesis does not address fully the question of self-healing networks. Nevertheless, it contributes to the understanding and the implementation of this concept in production cellular networks.

PhD defense of Mariem BEN YAHIA on May 10th, 2019

The defense of PhD thesis of Mariem Ben Yahia will be held on May 10th at  14:00 at IMT Atlantique, Rennes Campus , Petit Amphi. The thesis is entitled “Low Latency Video Streaming Solutions based on HTTP/2”. It was made in the framework of a CIFRE thesis with Orange Labs (Lannion).

Adaptive video streaming techniques encode the content at different levels of quality and split it into temporal segments. Before downloading a segment, the client runs an adaptation algorithm to determine the best level of quality to consider that matches the network resources. Besides, in the case of immersive video streaming this adaptation mechanism should also consider the head movement of a user watching the 360° video to maximize the quality of the viewed portion of the sphere. However, this adaptation may suffer from errors impacting negatively the end user’s quality of experience. In this case, an HTTP/1 client must wait for the download of the next segment to choose a suitable quality. In this thesis, we propose to use the HTTP/2 protocol instead to address this problem. First, we focus on the live streaming service. We design a strategy to discard video frames when the bandwidth is very variable in order to avoid the rebuffering events and the accumulation of delays. The customer requests each video frame in an HTTP/2 stream which allows to control the delivery of frames by leveraging the HTTP/2 features at the level of the dedicated stream. Second, we use the priority and reset stream features of HTTP/2 to optimize the immersive video streaming service. We propose a strategy to benefit from the improvement overtime of the user’s head movements prediction. The results show that HTTP/2 allows to optimize the use of network resources and to adapt the video delivery to the latencies required by each service.

(Français) Prix de la meilleure thèse “Futur et Rupture” pour Xavier Corbillon

Sorry, this entry is only available in French.

PhD defense of Rabah Guedrez on December 12th, 2018

The defense of PhD thesis of Rabah Guedrez will be held on December 12th at 09:00 at IMT Atlantique, Rennes Campus , Petit Amphi. The thesis is entitled “Enabling Traffic Engineering Over Segment Routing”. It was made in the framework of a CIFRE thesis with Orange Labs (Lannion).

Most major operators use MPLS technology to manage their network via signalling and label distribution protocols. However, these protocols are complex to deploy, maintain and troubleshooting is often very difficult. The IETF has initiated the standardization of a segment routing architecture based on a simple control plane, lightweight, easy-to-manage and instantiated on MPLS or IPv6. This architecture is based on the concept of source routing, in which the packet header carries the indications of the path to follow to reach its destination. Suitable for simple use cases and natively resistant to failure, more complex use cases require the resolution of technological issues for which we offer  several solutions. In this thesis carried out within Orange Labs, we were interested in the instantiation of the Segment Routing architecture on the MPLS transfer plan and more particularly in traffic engineering, particularly with resource reservation. We have proposed solutions to the problems related to the hardware limitation of current routers that do not allow the expression of all constrained paths. This work is divided into two parts: (i) the proposal of algorithms for computing and encoding segment routing paths in order to bypass hardware limitations. (ii) the definition of architectural requirements and the construction of a functional proof of concept. Finally, this thesis proposes new research issues to consolidate traffic engineering tools for segment routing.

Keywords :      Segment Routing, Ingénierie de trafic, Optimisations, IP/MPLS, SDN


PhD defense of Mahdi Ezzaouia on November 8th, 2018

The defense of PhD thesis of Mahdi Ezzaouia will be held on November 8th at 10:00 at IMT Atlantique, Rennes Campus , Petit Amphi. The thesis is entitled “Opportunistic resource allocation in multi-cell wireless networks”. It was made in the framework of a “co-tutelle” with Ecole Nationale d’Ingénieurs de Tunis (ENIT), Tunisia.


PhD defense of Xavier Corbillon on October 30th, 2018

The defense of PhD thesis of Xavier Corbillon will be held on October 30th at 10:15 at IMT Atlantique, Rennes Campus , Petit Amphi. The thesis title is “Enable the next generation of interactive video streaming”.


Omnidirectional videos, also denoted as spherical videos or 360° videos, are videos with pixels recorded from a given viewpoint in every direction of space. A user watching such an omnidirectional content with a Head Mounted Display (HMD) can select the portion of the video to display, usually denoted as viewport, by moving her head. To feel high immersion inside the content a user needs to see viewport with 4K resolution and 90 Hz frame rate. With traditional streaming technologies, providing such quality would require a data rate of more than 100 Mbps , which is far too high compared to the median Internet access bandwidth. In this dissertation, I present my contributions to enable the streaming of highly immersive omnidirectional videos on the Internet. We can distinguish six contributions : a viewport-adaptive streaming architecture proposal reusing a part of existing technologies ; an extension of this architecture for videos with six degrees of freedom ; two theoretical studies of videos with non-homogeneous spatial quality ; an open-source software for handling 360° videos ; and a dataset of recorded users’ trajectories while watching 360° videos. The work done during this thesis was published in eight international conferences and in one journal, and resulted in three “Best Paper Award”s.


Omnidirectional Video, 360°Video, Viewport-Adaptive Streaming, DASH, HEVC

PhD defense of Alassane Samba on October 29th, 2018

The defense of PhD thesis of Alassane Samba will be held on October 29th at 14:00 at IMT Atlantique, Rennes Campus , Petit Amphi. The thesis title is “Data science for services in operated networks”.