PhD Defence: “Navigation Among Movable Obstacles (NAMO) Extended to Social and Multi-Robot Constraints”, Benoit Renault, Amphi Chappe/Lamarr Building, 19th of December 2023 at 2 PM

The defense will take place on Tuesday 19th December at 2 PM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.


Navigation Among Movable Obstacles (NAMO) Extended to Social and Multi-Robot Constraints


As robots become ever more commonplace in human environments, taking care of ever more tasks such as cleaning, security or food service, their current limitations only become more apparent. One such limitation is of their navigation capability in the presence of obstacles: they always avoid them, and freeze in place when avoidance is impossible.

This is what brought about the creation of Navigation Among Movable Obstacles (NAMO) algorithms, expected to allow robots to manipulate obstacles as to facilitate their own movement. However, these algorithms were designed under the hypothesis of a single robot per environment, biasing NAMO algorithms into only optimizing the single robot’s displacement cost – without any consideration for humans or other robots. While it is desirable to endow robots with the human capability of moving obstacles, they must however do so while respecting social norms and rules of humans.

We have thus extended the NAMO problem as to take into account these new social and multi-robots aspects. By relying on the concept of affordance spaces, we have developed a social occupation cost model allowing the evaluation of the impact of moved objects on the environment’s navigability. We implemented (and improved) reference NAMO algorithms, in our open source simulation tool, and modified them so that they may plan compromises between robot displacement cost and social occupation cost of moved obstacles – resulting in improved navigability. We also developed an implicit coordination strategy allowing the concurrent execution of these same algorithms by multiple robots as is, without any explicit communication requirements, while preserving the no-collision guarantee; verifying the relevance of our social occupation cost model in the actual presence of other robots. As such, this work constitutes the first steps towards a Social and Multi-Robot NAMO.


    • Philippe Mathieu , Professeur des Universités, Université de Lille, CRISTAL, Rapporteur
    • Fabien Michel, Maître de Conférences HDR, Université Montpellier 2, LIRMM, Rapporteur
    • Julie Dugdale, Professeur des Universités, Université de Grenoble, LIG, Examinatrice
    • Rachid Alami, Directeur de Recherche CNRS émérite, LAAS, Toulouse, Examinateur
    • Olivier Simonin, Professeur des Universités, INSA-Lyon, CITI, Directeur de thèse
    • Jacques Saraydaryan, Enseignant Chercheur, CPE Lyon, CITI, Co-encadrant

PhD Defence: “Human and Network Mobility Management using Mobile Phone Data”, Solohaja Rabenjamina, Amphi Chappe/Lamarr Building, 29th of September 2023 at 2 PM

The defense will take place on Friday 29th December at 2 PM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.


Human and Network Mobility Management using Mobile Phone Data


Over the past decade, the increasing use of smartphones has led to a significant rise in the volume of data exchanged through mobile networks of telecommunications operators. Each new generation of mobile network generates more data than its predecessor. By 2027, it is estimated that 289 EB of data will be exchanged per month, with 62% originating from the 5G mobile network. This vast availability of data has opened up new research perspectives, particularly in the study of mobility. Mobile data enables studies on larger populations and extended geographical areas.

In this thesis, we demonstrate that the events described in mobile data can also be found in other data sources. Through comparisons between mobile data and sensors detecting human presence, we observe a reasonable correlation. However, certain events, such as synchronization of peak presence or end-of-day activity, exhibit less similarity. We also utilize mobile data to examine the impact of the COVID-19 lockdowns imposed by the French government on land usage in Paris. Our findings indicate that the first lockdown had a profound impact on mobility patterns and land utilization, while the second and third lockdowns had a lesser impact. Lastly, we leverage this data for the reconfiguration of the mobile network in managing user micro-mobility, known as handover. The eNodeBs, which constitute the access network, can have different profiles and categories. By distinguishing between mobile and stationary users, we can optimize resource allocation through network reconfiguration. Dynamic network reconfiguration, employing various eNodeB profiles, also enables resource savings for mobile users.


    • Marco FIORE, Directeur de Recherche, IMDEA Networks, Rapporteur
    • Vania CONAN, Habilité à Diriger des Recherches, Thales, Rapporteur
    • Aline CARNEIRO VIANA, Directeur de Recherche, INRIA, Examinatrice
    • Sahar HOTEIT, Maître de Conférences, Université Paris Saclay, Examinatrice
    • Stefano SECCI, Professeur des Universités, CNAM, Examinateur
    • Hervé RIVANO, Professeur des universités, INSA-Lyon, Directeur de thèse
    • Razvan STANICA, Maître de conférences HDR, INSA-Lyon, Co-directeur de thèse

PhD Defence: “Spatio-temporal Data Analysis for Dynamic Phenomenon Monitoring Using Mobile Sensors”, Ichrak Mokhtari, Amphi Chappe Building, 6th of June 2023 at 10 AM

The defense will take place on Tuesday 6th June at 10 AM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.


Spatio-temporal Data Analysis for Dynamic Phenomenon Monitoring Using Mobile Sensors


Monitoring air pollution in emergencies (industrial accidents, terrorist attacks, volcanic eruptions, etc.) is of utmost importance given the dramatic effects that the released pollutants can cause on both human health and the environment. In these situations, the pollution plume is strongly dynamic leading to a fast dispersion of pollutants in the atmosphere. Thus, the need for real-time response is very strong and a solution to get a precise mapping of pollution dispersion is needed to mitigate risks.

This thesis focuses on the monitoring of air pollution in emergencies using a fleet of drones, with three main areas of investigation: 1) the spatiotemporal prediction of pollution plume evolution; 2) the optimal planning of drones trajectories to improve pollution mapping; and 3) the development of a generic solution for dynamic pollution monitoring. Through this work, we
propose a spatio-temporal Deep Learning model for multi-point forecasting of pollution concentrations, and we built upon several uncertainty quantification techniques to make it more trustworthy. Furthermore, we examine and identify the main challenges related to the underlying phenomena as well as its emergency context, and we suggest a new systemic approach for monitoring dynamic air pollution based on aerial sensing, that combines Deep Learning approaches, with Data Assimilation techniques, while relying at the same time on adequate path planning
strategies. The framework is then extended to address the data scarcity issues encountered in such situations through a transfer learning solution based on physical models. Finally, we meticulously address the drones’ path planning problem to improve the air pollution mapping quality, and we provide a Multi-Agent Reinforcement Learning solution.

Keywords: Monitoring Dynamic Air Pollution, Spatio-temporal Forecasting, Deep Learning, Multi-Agent Reinforcement Learning, Drones.


  • NATALIZIO, Enrico Professeur des universités TII, Abu Dhabi Rapporteur
  • MITTON, Natalie Directrice de recherche INRIA Rapportrice
  • GARCIA, christophe Professeur des universités INSA-LYON Examinateur
  • CARNEIRO Viana, Aline Directrice de recherche INRIA Examinatrice
  • LABENTALl, Bérengère Directrice de recherche Université Gustave Eiffel Examinatrice
  • RIVANO, hervé Professeur des universités INSA-LYON Directeur de thèse
  • BECHKIT, Walid Maître de conférences INSA-LYON Co-directeur de thèse

PhD Defence: “Resilient IoT-based Monitoring System for the Nigerian Oil and Gas Industry”, Safuriyawu Ahmed, amphi est, bâtiment des humanités, 16th of December 2022 at 10 AM

The defense will take place on Friday 16th December at 10 AM in the est amphi of the Humanities building, Insa-Lyon, Villeurbanne.



Resilient IoT-based Monitoring System for the Nigerian Oil and Gas Industry




Pipeline failures in crude oil transportation occur due to ageing infrastructure, third-party interferences, equipment defects and naturally occurring failures. Consequently, hydrocarbons are released into the environment resulting in environmental pollution, ecological degradation, and unprecedented loss of lives and revenue. Hence, multiple leakage detection and monitoring systems (LDMS) are employed to mitigate such failures. More recently, these LDMS include Wireless Sensor Networks (WSN) and Internet of Things (IoT)-based systems. While they are proven more efficient than other LDMS, many challenges exist in their adoption for pipeline monitoring. These include fault tolerance, energy consumption, accuracy in leakage detection and localisation, and high false alarms, to cite a few.

Therefore, our work seeks to address some of these challenges in implementing IoT-based systems for crude oil pipelines in a resilient end-to-end manner. Specifically, we consider the aspect of accurate leakage detection and localisation by introducing a unique node placement strategy based on fluid propagation for sensitive and multi-sized leakage detection. We also propose a new distributed leakage detection technique (HyDiLLEch) in the WSN layer. It is based on a fusion of existing leakage detection techniques such as the negative pressure wave method, gradient-based method, and pressure point analysis. With HyDiLLEch, we efficiently eliminate single points of failure.

Furthermore, we implement fault-tolerant data and service management in the fog layer utilising the Nigerian National Petroleum Corporation (NNPC) pipeline network as a use case. The problem is modelled as a regionalised data-driven game against nature on the NNPC pipelines. Our proposed regionalised solution (R-MDP) using reinforcement learning optimises accuracy and fault tolerance while minimising energy consumption.

Overall, our system guarantees resiliency to failures and efficiency in terms of detection and localisation accuracy and energy consumption.



  • Stolf, Patricia, Professeur des Universités, Université de Toulouse, Rapporteure
  • Guidec, Frédéric, Professeur des Universités, Université Bretagne Sud, Rapporteur
  • Menaud, Jean-Marc, Professeur des Universités, IMT-Atlantique, Examinateur
  • Caron, Eddy, Maître de Conférences, ENS Lyon, Examinateur
  • Takruri-Rizk, Haifa, Professeur des Universités, University of Salford, Examinatrice
  • Silva, Bhagya Nathali, Senior Lecturer, University of SriJayawardenepura, Examinatrice
  • Le Mouël, Frédéric, Professeur des Universités, INSA Lyon, Directeur de thèse
  • Stouls, Nicolas, Maître de Conférences, INSA Lyon, Co-Directeur de thèse
  • Yusuf, Kabir, Docteur en gestion des ressources environnementales, PTDF/SAPZ, Invité

PhD Defence: “Contributions to the development of passive RFID tag-to-tag communications for the Internet of Things”, Tarik Lassouaoui, amphi ouest, batiment des humanités, 16th of December 2022 at 10 AM

The defense will take place on Friday 16th December at 10 AM in the west amphi of the Humanities building, Insa-Lyon, Villeurbanne.


Contributions to the development of passive RFID tag-to-tag communications for the Internet of Things


With the emergence of cognitive sensor networks, and in particular the IoT (Internet of Things), passive RFID (Radio Frequency Identification) UHF (Ultra High Frequency) technology is evolving with new functionalities. New types of applications going beyond the classics such as logistics, security and traceability are being developed. Still benefiting from unitary identification, new types of tags, called augmented tags, are appearing integrating new capacities such as environmental sensitivity, cognitive behaviour, data processing, communication between tags, etc. In this context, the objective of this thesis is to propose strategies and methods to optimize communications between tags, called tag-to-tag “tag-to-tag” (T2T) communications. This new type of radio link, between directly communicating tags, relies on the presence of an external radio frequency (RF) source and is based on the principle of retro-modulation. In particular, the scenarios analyzed are projected within the framework of the Spie ICS – INSA Lyon chair, which focuses on the IoT.

This thesis more specifically targets the application domain of UHF RFID for which the concept of T2T communications has been proposed and demonstrated since 2011, but with relatively little work done so far. In a T2T RFID system, two RFID tags (passive or semi- passive) communicate with each other directly without going through the reader. One of the tags plays the role of “reader tag”: in the presence of an RF source (for example an RFID reader) in its vicinity, it emits binary information by retro-modulation (or backscatter by switching charges), by switching the load seen by its antenna on two distinct impedances, thus reflecting two distinct power levels (modulation here considered in amplitude). The other tag plays the role of “receiver tag”: it receives the information transmitted and demodulates it.

In the traditional case of UHF RFID, the reader emits, in accordance with the standard, a modulated signal with a high modulation depth in order to facilitate the detection of the message transmitted by the tag. The tag responds by retro modulation and the signal it returns is then a signal where the two levels of information (in the case of amplitude modulation) are not very distinct and noisy. However, the player’s demodulator is very efficient, based on quadrature synchronous demodulation, it has very good sensitivity. In the case of T2T communication, a fundamental difference is that the detection is here performed by the second tag which is in the vicinity of the reader tag. Consequently, on the one hand, the performance of the receiver (that of a “simple” RFID tag) is much more limited, while the modulated signal is not necessarily at high modulation depth. And on the other hand, the two tags interact. This inter-tag electromagnetic coupling impacts in particular radiation patterns and impedances, and moreover, it depends on the mutual positions of the two tags, more precisely on their antennas (distance separating them, relative orientation, etc.), which leads to high variability the characteristics of the T2T system (and therefore its performance). In addition, there is the impact of the relative position of the external RF source with the pair of tags, which significantly modifies the characteristics of the retro-modulated signals.

The main challenge of the thesis is to determine a framework that can take into account all the factors (signal, component, circuit and system) impacting T2T communication with the aim of evaluating performance, particularly in terms of communication rate. binary error, a metric conventionally used in the field of telecommunications.

Keywords: Backscattering, passive tags, tag to tag communications, UHF RFID


  • Bergeret Emmanuel Professor Université Clermont Auvergne Reviewer
  • Breard Arnaud Professor Ecole Centrale de Lyon Examiner
  • Lepage Anne-Claire Associate Professor Telecom Paris Examiner
  • Lienard Martine Professor Université de Lille Examiner
  • Vena Arnaud Associate Professor HDR Université de Montpellier Reviewer
  • Villemaud Guillaume Associate Professor HDR INSA-Lyon Thesis director
  • Hutu Florin-Doru Associate Professor INSA-Lyon Co-director
  • Duroc Yvan Professor Université Claude Bernard Lyon 1 Co-director

PhD Defence: “Symmetric semi-discrete optimal transport for mesh interpolation”, Agathe Herrou, C5 room, Nautibus building, 20th of October 2022 at 1.30 PM


The defenseIt will take place on Thursday 20th october at 1:30 PM in the C5 room of the Nautibus building, Villeurbanne.



Symmetric semi-discrete optimal transport for mesh interpolation




This thesis aims to develop geometric methods to approximate displacement interpolation, derived from optimal transport. Optimal transport is a mathematical theory modeling movements of matter under a cost minimization constraint, with many applications in physics, computer graphics and geometry. The minimum displacement cost between two distributions defines a distance, which itself is at the origin of displacement interpolation. This interpolation may under certain conditions present discontinuities, that the discretized approximations of the optimal transport do not always successfully capture. The work of this thesis aims to develop an approximation that captures these discontinuities well. Our method relies on semi-discrete optimal transport, where only one of the distributions is discretized, thus accurately capturing the discontinuities of the distribution that remains continuous. The transport plans thus obtained partition the continuous distribution into cells associated with the samples of the discretization. A semi-discrete optimal transport plan can thus be assimilated to a power diagram made up of these cells. This variant of optimal transport however has the disadvantage of breaking the symmetry between the two distributions. We start by formalizing our problem as the search for a pair of transport plans coupled through the barycenters of their cells. We then present an algorithm for calculating these coupled transport plans. This first algorithm is based on a classical alternating algorithm scheme, successively computing the transport plans and the barycenters of their cells until convergence. The results obtained from this algorithm allow to interpolate between the initial distributions while maintaining a satisfactory precision, in particular when it comes to discontinuities, including when the discretization of the distributions is done with relatively few points. We then present our exploration of optimization methods for solving the same problem. These methods express the constraints of our problem as a critical point of a functional, and aim to reach these points using algorithms such as Newton’s method. However, this approach did not yield conclusive results, as the functions involved were too noisy to lend themselves well to optimization algorithms.

Keywords: Optimal transport, Interpolation, Optimization, Algorithmic geometry


    • Julie Delon (reviewer), Université Paris Cité
    • Boris Thibert (reviewer), Université Grenoble Alpes
    • Dominique Attali (examiner), CNRS/Université Grenoble Alpes
    • Filippo Santambrogio (examiner), Université Lyon 1
    • Nicolas Bonneel (advisor), CNRS/Université Lyon 1
    • Julie Digne (co-advisor), CNRS/Université Lyon 1
    • Bruno Lévy (co-advisor), Inria Nancy Grand Est

PhD Defence: “User Association in Flexible and Agile Mobile Networks”, Romain PUJOL, amphi, Chappe Building, 6th of July 2022 at 2 PM


The defense will take place on Wenesday, July 6 at 2 pm, in the amphitheatre of the Telecommunications Department (Claude Chappe building), INSA Lyon, Villeurbanne.

The presentation will be available online:



User Association in Flexible and Agile Mobile Networks




The user association process in cellular networks consists in choosing the base station with which the user equipment will negotiate radio resources. The current association is based on measuring the signal strength received by the user equipment from each of the base stations. The association must now deal with the diversification of user application needs and the growing heterogeneity of cellular networks.


In this thesis, we show experimentally that the current association process has reached its limits, that it is agnostic of the configurations of the base stations and that it does not allow to control the throughput that the user equipment will obtain following the association. We set up for the realization of our measurements an experimental platform based on the software suite of cellular networks srsRAN and software radios USRP NI-2091.


We propose in this thesis a new metric to be used during the association process. This metric, broadcasted by the base station, is a load information which in our case will be represented by the number of user equipment connected to the base station. We also discuss other metrics that can be used as load information. We show, once again experimentally, by modifying the source code of the srsRAN software suite ourselves, that if the user equipment takes this load information into account in the association process, the association decision is improved in 22% of cases.



  • Tara ALI YAHIA, Associate Professor HDR, Université Paris Saclay (Reviewer)
  • Vania CONAN, HDR, Thales (Reviewer)
  • Véronique VEQUE, Professor, Université Paris Saclay (Examiner)
  • Frédéric LAUNAY, Associate Professor, IUT de Poitiers (Examiner)
  • Stéphane FRENOT, Professor, INSA-Lyon (Examiner)
  • André-Luc BEYLOT, Professor, ENSEEIHT (Examiner)
  • Fabrice VALOIS, Professor, INSA-Lyon (Thesis supervisor)
  • Razvan STANICA, Associate Professor HDR, INSA-Lyon (Thesis co-supervisor)

PhD Defence: “Activity Models and Bayesian Estimation Algorithms for Wireless Grant-Free Random Access”, Lélio CHETOT, amphi, Chappe Building, 7th of July 2022 at 2 PM


The defense will take place on Thursday, July 7 at 2 pm, in the amphitheatre of the Telecommunications Department (Claude Chappe building), INSA Lyon, Villeurbanne.

The presentation will be available online:



Activity Models and Bayesian Estimation Algorithms for Wireless Grant-Free Random Access




The new 5G’s wireless networks have recently started to be deployed all around the world. With them, a large spectrum of services are about to emerge, resulting in new stringent requirements so that 5G targets performance exceed that of 4G by a factor of 10. The services are centered around the use cases of enhanced mobile broadband (eMBB), ultra reliable and low-latency communication (uRLLC) and massive machine-type communication (mMTC) where each of which has required the ongoing development of key new technologies. Many of these technologies will also play an important role in the emergence of 6G.

In this thesis, the focus is on grant-free RA (GFRA) as an enabler of uRLLC and mMTC. GFRA is a new protocol introduced in 5G new radio (5G-NR) for reducing the data overhead of the random access (RA) procedure. This results in a significant reduction in the latencies of the user equipments (UEs) access to a connected medium via an access point (AP). Achieving efficient GFRA is of key importance for many 5G applications, e.g. for large scale internet of things (IoT) wireless networks. The study of new non-orthogonal multiple access (NOMA) signal processing techniques is then considered. Using tools from the theory of compressed sensing (CS), and particularly from Bayesian CS, new algorithms within the family of approximate message passing (AMP) are developed to address the joint active user detection and channel estimation (AUDaCE) problem. The active user detection is crucial to properly identify transmitting UEs within the context of large-scale dense network; the channel estimation is equally important so that an AP can reliably transmit back data to the detected UEs.

In this thesis, in contrast to existing work on this topic, the AUDaCE is studied for wireless networks where the activity of the UEs is assumed to be correlated, as is typical for many large-scale dense networks. To this end, two new activity models are introduced. The first one assumes that the activity of the UEs in the network can be modeled via group-homogeneous activity (GHomA) where devices in the same group have common pairwise correlations and marginal activity probabilities. The second model accounts for more general dependence structure via group-heterogeneous activity (GHetA). Novel approximate message passing algorithms within the hybrid GAMP (HGAMP) framework are developed for each of the models. With the aid of latent variables associated to each group for modeling the activity probabilities of the UEs, the GHomA-HGAMP algorithm can perform AUDaCE for GFRA leveraging such a group homogeneity. When the activity is heterogenous, i.e. each UE is associated with a latent variable modeling its activity probability correlated with the other variables, it is possible to develop GHetA-HGAMP using the copula theory.

Extensive numerical studies are performed, which highlight significant performance improvements of GHomA-HGAMP and GHetA-HGAMP over existing algorithms (modified generalized AMP (GAMP) and group-sparse HGAMP (GS-HGAMP)), which do not properly account for correlation in activity. In particular, the channel estimation and active user detection capability are enhanced in many scenarios with up to a 4dB improvement with twice less user errors.

As a whole, this thesis provides a systematic approach to AUDaCE for wireless networks with correlated activities using tools from Bayesian CS. We then conclude by showing how it could be used for multi-carrier orthogonal frequency division multiplexing (OFDM) scenarios with possible extensions for grant-free (GF) data transmission leveraging joint data recovery, active user detection and channel estimation (DrAUDaCE).



  • Catherine DOUILLARD, Professor @ IMT Atlantique, Reviewer
  • Dejan VUKOBRATOVIC, Professor @ Univ. Novi Sad, Reviewer
  • Aline ROUMI, Senior Research Scientist @ INRIA Rennes, Examiner
  • Philippe CIBLAT, Professor @ Telecom Paris, Examiner
  • Cedomir STEFANOVIC, Professor @ Univ. Aalborg, Examiner
  • Jean-Marie GORCE, Professor @ INSA Lyon, Director
  • Malcolm EGAN, Research Scientist @ INRIA Lyon, Supervisore

PhD Defence: “Large-scale Automatic Learning of Autonomous Agent Behavior with Structured Deep Reinforcement Learning”, Edward Beeching, amphi, Chappe Building, 3rd of May 2022 at 10:00 AM


The defense willtake place on Tuesday, May 3 at 10:00 am, in the amphitheatre of the Telecommunications Department (Claude Chappe building), INSA Lyon, Villeurbanne.

The presentation will be available on Youtube at the following link:



Large-scale Automatic Learning of Autonomous Agent Behavior with Structured Deep Reinforcement Learning




Autonomous robotic agents have begun to impact many aspects of our society,with application in automated logistics, autonomous hospital porters, manufacturing and household assistants. The objective of this thesis is to explore Deep Reinforcement Learning approaches to planning and navigation in large and unknown 3D environments. In particular, we focus on tasks that require exploration and memory in simulated environments. An additional requirement is that learned policies should generalize to unseen map instances. Our long-term objective is the transfer of a learned policy to a real-world robotic system. Reinforcement learning algorithms learn by interaction. By acting with the objective of accumulating a task-based reward, an Embodied AI agent must learn to discover relevant semantic cues such as object recognition and obstacle avoidance, if these skills are pertinent to the task at hand. This thesis introduces the field of Structured Deep Reinforcement Learning and then describes 5 contributions that were published during the PhD.

We start by creating a set of challenging memory-based tasks whose performance is benchmarked with an unstructured memory-based agent. We then demonstrate how the incorporation of structure in the form of a learned metric map, differentiable inverse projective geometry and self-attention mechanisms; augments the unstructured agent, improving its performance and allowing us to interpret the agent’s reasoning process.

We then move from complex tasks in visually simple environments, to more challenging environments with photo-realistic observations, extracted from scans of real-world buildings. In this work we demonstrate that augmenting such an agent with a topological map can improve its navigation performance. We achieve this by learning a neural approximation of a classical path planning algorithm, which can be utilized on graphs with uncertain connectivity.

From work undertaken over the course of a 4-month internship at the research and development department of Ubisoft, we demonstrate that structured methods can also be used for navigation and planning in challenging video game environments. Where we couple a lower level neural policy with a classical planning algorithm to improve long-distance planning and navigation performance in vast environments of 1km×1km. We release an open-source version of the environment as a benchmark for navigation in large-scale environments.

Finally, we develop an open-source Deep Reinforcement Learning interface for the Godot Game Engine. Allowing for the construction of complex virtual worlds and the learning of agent behaviors with a suite of state-of-the-art algorithms. We release the tool with a permissive open-source (MIT) license, to aid researchers in their pursuit of complex embodied AI agents.




    • Mme. Elisa Fromont – Université de Rennes 1 – Rapporteur
    • M. David Filliat – ENSTA Paris – Rapporteur
    • M. Cédric Démonceaux – Université de Bourgogne – Examinateur
    • M. Karteek Alahari – INRIA Grenoble – Examinateur
    • Mme. Christine Solnon – INSA-Lyon – Examinateur
    • M. Olivier Simonin – INSA-Lyon – Directeur de thèse
    • M. Jilles Dibangoye – INSA-Lyon – Co-encadrant de thèse
    • M. Christian Wolf – Naver Labs Europe – Co-directeur de thèse

PhD Defence: “Applications of Deep Learning to the Design of Enhanced Wireless Communication Systems”, Mathieu Goutay, Showcase room, Chappe Building, 28th of January 2022 at 2:00 PM

This thesis has been done within Nokia Bell Labs France and the Maracas team of the CITI laboratory.

The defense willtake place on Friday, January 28 at 2:00 pm, in the showcase room of the Telecommunications Department (first floor), INSA Lyon, Villeurbanne.

The presentation will be available on Zoom at the following link:



Applications of Deep Learning to the Design of Enhanced Wireless Communication Systems




Innovation in the physical layer of communication systems has traditionally been achieved

by breaking down the transceivers into sets of processing blocks, each optimized independently based on mathematical models. This approach is now challenged by the ever-growing demand for wireless connectivity and the increasingly diverse set of devices and use-cases. Conversely, deep learning (DL)-based systems are able to handle increasingly complex tasks for which no tractable models are available. By learning from the data, these systems could be trained to embrace the undesired effects of practical hardware and channels instead of trying to cancel them. This thesis aims at comparing different approaches to unlock the full potential of DL in the physical layer.

First, we describe a neural network (NN)-based block strategy, where an NN is optimized to replace one or multiple block(s) in a communication system. We apply this strategy to introduce a multi-user multiple-input multiple-output (MU-MIMO) detector that builds on top of an existing DL-based architecture. The key motivation is to replace the need for retraining on each new channel realization by a hypernetwork that generates optimized sets of parameters for the underlying DL detector. Second, we detail an end-to-end strategy, in which the transmitter and receiver are modeled as NNs that are jointly trained to maximize an achievable information rate. This approach allows for deeper optimizations, as illustrated with the design of waveforms that achieve high throughputs while satisfying peak-to-average power ratio (PAPR) and adjacent channel leakage ratio (ACLR) constraints. Lastly, we propose a hybrid strategy, where multiple DL components are inserted into a traditional architecture but trained to optimize the end-to-end performance. To demonstrate its benefits, we propose a DL-enhanced MU-MIMO receiver that both enable lower bit error rates (BERs) compared to a conventional receiver and remains scalable to any number of users.

Each approach has its own strengths and shortcomings. While the first one is the easiest to implement, its individual block optimization does not ensure the overall system optimality. On the other hand, systems designed with the second approach are computationally complex and do not comply with current standards, but allow the emergence of new opportunities such as high-dimensional constellations and pilotless transmissions. Finally, even if the block-based architecture of the third approach prevents deeper optimizations, the combined flexibility and end-to-end performance gains motivate its use for short-term practical implementations.




    • Reviewer: Didier LE RUYET, Professor, CNAM, Paris, France
    • Reviewer: Charlotte LANGLAIS, Permanent Research Staff, IMT Atlantique, Brest, France
    • Examiner: Inbar FIJALKOW, Professor, ENSEA, Cergy, France
    • Examiner: Stephan TEN BRINK, Professor, University of Stuttgart, Stuttgart, Allemagne
    • Thesis supervisor: Jean-Marie GORCE, Professor, INSA Lyon, Villeurbanne, France
    • Thesis co-supervisor: Jakob HOYDIS, Principal Research Scientist, Nvidia, France*
    • Thesis co-supervisor Fayçal AIT AOUDIA, Senior Research Scientist, Nvidia, France*

* Previously at Nokia Bell Labs France