Mathieu Cunche, full professeur at the CITI Laboratory, is cited in two articles from Le Monde :
Données personnelles en vente libre _ les « data brokers », une industrie hors de contrôle
Biography
https://perso.citi-lab.fr/mcunche/index.html
Speaker
Sheng YANG
When / Where
13th February 2025 ; room TD-C ; Heddy Lamarr building INSA Lyon; Villeurbanne
This seminar is open to everyone and is recognized by the EEA doctoral school for doctoral training hours. We will have a sign-in sheet for PhD students who wish to attend the seminar (in person).
visio
https://insa-lyon-fr.zoom.us/j/92678255340<https://insa-lyon-fr.zoom.us/j/92678255340>
Title
From Bayesian Statistics to Large-Scale MIMO Communications
Abstract
Large-scale MIMO systems have emerged as a cornerstone for next-generation wireless communication networks. While extended research has been conducted on signal processing and transceiver design in these systems, the fundamental Shannon capacity limit remains elusive in many settings, particularly in the presence of system non-linearities. In this talk, we explore the connection between statistics and communication, and introduce a novel approach that leverages information-theoretic asymptotics from Bayesian statistics to derive the Shannon capacity of such systems. We reveal the critical role of the Fisher information and Jeffreys’ prior in this characterization, and
demonstrate how to apply this method to derive the asymptotic capacity of various channel models. Examples include the MIMO channels with 1-bit ADC, clipping, phase noise, and imperfect channel state information.
Biography
Sheng Yang received the B.E. degree in electrical engineering from Jiaotong University, Shanghai, China, in 2001, and both the engineer degree and the M.Sc. degree in electrical engineering from Telecom ParisTech, Paris, France, in 2004, respectively. In 2007, he obtained his Ph.D. from Université de Pierre et Marie Curie (Paris VI).
From October 2007 to November 2008, he was with Motorola Research Center in Gif-sur-Yvette, France, as a senior staff research engineer.
Since December 2008, he has joined CentraleSupélec, Paris-Saclay University, where he is currently a full professor. From April 2015, he also holds an honorary associate professorship in the department of electrical and electronic engineering of the University of Hong Kong (HKU).
He received the 2015 IEEE ComSoc Young Researcher Award for the Europe, Middle East, and Africa Region (EMEA). He was an associate editor of the IEEE transactions on wireless communications from 2015 to 2020.
He is currently an associate editor of the IEEE transactions on information theory.
The Emeraude team of the CITI laboratory are organizing the 2025 “Journées de l’Informatique Musicale” (JIM) and Linux Audio Conference (LAC) jointly on June 23-28, 2025 in Lyon : https://jimlac25.inria.fr/
The calls for papers for these 2 events are published here :
– https://jimlac25.inria.fr/lac/#call-for-papers-presentations-demos-workshops
The defense will take place on wednesday 5th february at 2 PM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.
Title
Seamless Continuous Integration / Continuous Delivery (CI/CD) for Software Defined Vehicles
Abstract
Driven by the rapid increase in the number of Electronic Control Units (ECUs), current automotive software systems face growing complexity while advancements in software architecture are well behind. This imbalance has resulted in higher system complexity, important financial costs, and significant challenges in maintaining and deploying new services in vehicles. The thesis explores the potential of adopting Continuous Integration/Continuous Delivery (CI/CD) pipelines for software-defined vehicles, focusing on several critical aspects: secure software deployment, adaptability of in-vehicle software, and optimization of performance using edge computing.
The contributions of the thesis are manifold: (1) A comprehensive taxonomy of key findings related to the transformation of automotive ICT systems, (2) A proposal for a blockchain-based multi-automaker software store to manage updates and dependencies, (3) The development of a virtualization framework for multi-microcontroller systems and an evaluation of these OS-level virtualization solutions for in-vehicle systems, (4) A software orchestration framework that prioritizes criticality and optimizes resource allocation in heterogeneous environments, and finally (5) A consensus algorithm to efficiently offload functions to edge-computing IoT nodes, optimizing resource use in automotive cloud-edge systems.
By addressing these issues, the thesis contributes to the future of automotive ICT systems, proposing innovative methods that strike a balance between flexibility and performance in managing software complexity within the evolving landscape of connected, autonomous vehicles.
Jury
The jury deliberated to highlight a junior colleague from our community.
The 2024 jury grants the 2024 Junior Researcher Award of the GDR RSD to Oana Iova.
Oana Iova has been an associate professor at INSA Lyon since 2017 and conducts her research at the CITI laboratory.
For more information : https://gdr-rsd.cnrs.fr/laureate-du-prix-chercheur-junior-2024/
The defense will hold on monday, December 16th, at 9.30 AM in room ,020G at ENSSAT Lannion
Title
Powering low-power Wake-up Radios with RF energy harvesting.
Abstract
Due to the massive deployment of connected devices in the context of the Internet of Things (IoT), powering them exclusively with cables or batteries is not efficient. This thesis explores the use of radiofrequency (RF) energy as an alternative power source for wake-up radios (WuRx) in wireless sensors, thereby reducing their reliance on batteries. The first challenge is to develop an RF energy harvesting circuit capable of providing a regulated voltage from low power levels. An innovative solution is proposed, based on Schottky diode RF rectifiers incorporating the inductive technique. This circuit ensures the operation of an energy management system that powers a semi-active WuRx and stores excess energy when higher power levels are available.
Given the intermittent nature of RF energy, the second challenge is to adapt the WuRx’s energy consumption by modulating its quality of service, defined as the percentage of processed signals among those received, based on the harvested energy.
Jury
* Nathalie DELTIMPLE, Professor at Bordeaux INP, Reviewer
* Christian VOLLAIRE, Professor at Ecole Centrale Lyon, Reviewer
* Daniela DRAGOMIRESCU, Professor at INSA de Toulouse, Examiner
* Laurent CLAVIER, Professor at IMT Nord Europe, Examiner
* Dominique MORCHE, Research Director at CEA-LETI, Invited
* Matthieu GAUTIER, Professor at Univ. de Rennes,Thesis Director
* Guillaume VILLEMAUD, Assoc. Prof. at INSA de Lyon, INSA de Lyon,Thesis Co-Director
* Olivier BERDER, Professor at Univ. de Rennes,Supervisor
* Florin-Doru HUTU, Assoc. Prof. at INSA de Lyon, INSA de Lyon,Supervisor
The defense will take place on Monday December 16 at 14h in the Amphi Huma Ouest at Insa Lyon.
Title
Uplink Resource Allocation Methods for Next-Generation Wireless Networks
Abstract
Facing the diversity of communication needs of 5G networks and the future 6G, resource allocation is considered as a key enabler to increase the number of devices, the data rate or the reliability of the communication links. In MTC networks, recent work has proposed to adapt the temporal resource allocation as a function of the underlying process
driving the activity of the devices. This thesis firstly focuses on the impact of having only limited knowledge of the underlying process, and proposes methods to mitigate the bias induced by the lack of knowledge.
Secondly, an algorithm for the joint optimization of the temporal resource allocation and the transmit power of the devices is proposed. The algorithm ensures that devices that are likely to transmit on the same resources do so with a sufficient power diversity to ensure their decodability by the BS. Finally, in networks with an eMBB objective, we
propose to jointly optimize the power, the frequency resources used, as well as the number of parallel data streams used by the devices. Our simulation study shows that our joint optimization outperforms current 5G baselines for which these parameters are common to all devices of the cell.
Jury
* LOSCRI Valeria, Directrice de Recherche, Inria Lille, Rapporteur
* LIVA Gianluigi, Chercheur, German Aerospace Center, Rapporteur
* POPOVSKI Petar, Professeur, Aalborg University, Examinateur
* FIJALKOW Inbar, Professeure, ENSEA, Examinatrice
* VALCARCE Alvaro, Ingénieur de Recherche, Nokia Bell Labs, Examinateur
* ADJHI Cédric, Chargé de Recherche, Inria Saclay, Examinateur
* GORCE Jean-Marie, Professeur, INSA Lyon, Directeur de thèse
* EGAN Malcolm, Chargé de Recherche, Inria Lyon, Co-encadrant
The defense will take place on november 25 at 9 AM
Title
Learning spatial representations for single-task navigation and multi-task policies
Abstract
Autonomously behaving in the 3D world requires a large set of skills, among which are perceiving the surrounding environment, representing it precisely and efficiently enough to keep track of the past, making decisions, and acting to achieve specified goals. Animals, for instance humans, stand out by their robustness when it comes to acting in the
world. In particular, they can efficiently generalize to new environments but are also able to rapidly master many tasks of interest from a few examples. We will study how artificial neural networks can be trained to acquire a subset of these abilities. We will first focus on training neural agents to perform semantic mapping, both from augmented supervision signal and with proposed neural-based scene representations.
Neural agents are often trained with Reinforcement Learning (RL) from a sparse reward signal. Guiding the learning of scene mapping abilities by augmenting the vanilla RL supervision signal with auxiliary spatial reasoning tasks will help navigate efficiently. Instead of modifying the training signal of neural agents, we will also see how incorporating specific neural-based representations of semantics and geometry within the architecture of the agent can help improve performance in goal-driven navigation. Then, we will study how to explore a 3D environment to build neural representations of space that are as satisfying as possible based on robotic-oriented metrics we will propose. Finally, we will move from navigation-only to multi-task agents, and see how important it is to tailor visual features from
sensor observations to the task at hand to perform a wide variety of tasks, but also to adapt to new unknown tasks from a few demonstrations.
Jury
* Ivan Laptev, Directeur de Recherche (INRIA Paris / MBZUAI), Rapporteur
* Karteek Alahari, Directeur de Recherche (INRIA Grenoble), Rapporteur
* Nicolas Thome, Professeur des Universités (Sorbonne Université), Examinateur
* Georgia Chalvatzaki, Full Professor (TU Darmstadt), Examinatrice
* Laëtitia Matignon, Maître de Conférences (UCBL), Co-Directrice de thèse
* Olivier Simonin, Professeur des Universités (INSA Lyon), Co-Directeur de thèse
* Christian Wolf, Principal Scientist (Naver Labs Europe), Co-Directeur de thèse
The defense will take place the 5th december at 9 AM at the library Marie-Curie INSA-Lyon
Title
Health Data: Exploring Emerging Privacy Enhancing Mechanisms
Abstract
Health data represents a large volume of information, generated daily and sensitive by nature. However, sharing this data is essential for advancing research and, ultimately, improving patient care. The use of medical data faces limitations due to its sensitivity and the need to ensure confidentiality, which is governed by current regulations. This
necessitates enhanced protection. Interest in alternatives to sharing raw data, such as pseudonymization or anonymization, is increasing alongside the growing need for access to training data for the use of artificial intelligence, which requires large amounts of data to function effectively as a medical assistant.
In this thesis, we explore new privacy-preserving mechanism made possible by the rapid advancements in artificial intelligence. More specifically, my analysis focuses on improving alternatives to the centralization of sensitive data: federated learning, a decentralized method of training artificial intelligence models that do not need sensitive data sharing, as well as synthetic data generation, which creates artificial data similar statistical properties to real data.
Given the lack of consensus on evaluating the privacy of these new approaches, our work focuses on the systematic measurement of privacy leakage and the balance with the utility of synthetic data or the federated learning model. My contributions include a mechanism to enhance the privacy properties of federated learning, as well as a new method for conditional synthetic data generation. This thesis aims to contribute to the development of more robust frameworks for the secure sharing of health data, in compliance with regulatory requirements, thereby facilitating innovations in healthcare.
Jury
* Sonia BEN MOKHTAR, Directrice de Recherche, CNRS/INSA-Lyon, Examiner,
*Szilvia LESTYAN, Docteure-Ingénieure de Recherche, INRIA, Examiner,
* Jérémie DECOUCHANT, Professeur des universités, Université de Delft, Examiner,
* Benjamin NGUYEN, Professeur des universités, INSA-CVL,Thesis Reviewer,
* Emmanuel VINCENT, Directeur de Recherche, INRIA,Thesis Reviewer,
On Thursday, October 24th, the teams of the CITI laboratory gathered at La Commune (Lyon 07) for their welcome day. It was an opportunity to review the laboratory’s roadmap and introduce the new faces, including the BioTic team, which will soon be joining the laboratory.
For more information : https://intranet.insa-lyon.fr/actualites/le-laboratoire-citi-fait-sa-rentree