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: https://insa-lyon-fr.zoom.us/j/91599742042

 

Title

User Association in Flexible and Agile Mobile Networks

 

 

Abstract

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.

 

Jury

  • 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: https://youtu.be/hX3t9pKPcoc

 

Title

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

 

 

Abstract

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).

 

Jury

  • 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: https://youtu.be/k7dh-thqbSk

 

Title

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

 

 

Abstract

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.

 

 

Jury

    • 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: https://insa-lyon-fr.zoom.us/j/96199554997

 

Title

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

 

 

Abstract

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.

 

 

Jury

    • 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


PhD Defence: “On the Performance of Spatial Modulation and Full Duplex Radio Architectures”, Yanni Zhou, Amphitheater, CITI, 10th of December 2021 at 10:00 PM

The defense will be held in the Amphitheater, Claude Chappe, and will be streamed live here.

 

Title

On the Performance of Spatial Modulation and Full Duplex Radio Architectures

 

 

Abstract

Index modulation techniques have exhibited great potential in the scenarios foreseen in next-generation wireless networks. Applying in the spatial domain, spatial modulation (SM) as a single radio-frequency (RF) multiple-input–multiple-output (MIMO) solution has attracted wide attention. The SM system has only one transmitting antenna activated for each time slot which results in low system complexity and cost. It exploits the index of the transmitting antennas to convey additional information bits.

To analyze the SM performance, a simulated framework over the time-varying Rician fading channel is built with ADS and Matlab software and a channel state information (CSI) detector is highlighted. The simulation results are verified by the experimental implementation based on the National Instruments (NI) PXI chassis hardware and LabVIEW programming environment. In the practical analysis, two models of the propagation environments are considered, where a channel sounding method is employed in order to extract the channel coefficients.

Despite issues on system complexity and cost, a shortage of spectrum resources can also restrict the development of mobile communications technology. Full duplex (FD) communications have been developed to double the radio link data rate and spectral efficiency through simultaneous and bidirectional communication. The main challenge of FD systems is self-interference (SI), which is caused by the coupling of the transmitting antenna with the receiving one. The combination of FD and SM will not only maintain spectral efficiency but also decrease the complexity of the self-interference cancellation (SIC) because of the single RF chain.

Based on these, a full duplex spatial modulation (FDSM) system is proposed as well as the SIC method. Moreover, the impact of SIC accuracy on the system performance is studied. We focus on the FDSM system imperfections including IQ imbalance, phase noise, power amplifier (PA) nonlinearities and RF switch nonidealities. The bit error rate (BER) performance under different scenarios with these imperfections is analyzed, along with the estimation and cancellation method.

 

 

Jury

  • Marco Di Renzo, Research Director at CNRS, Reviewer
  • Matthieu Crussière, Professor at INSA-Rennes, Reviewer
  • Christelle Aupetit-Berthelemot, Professor at Université de Limoges, Examiner
  • Taneli Riihonen, Associate Professor at Tampere University, Examiner
  • Jean-Marie Gorce, Professor at INSA-Lyon, Examiner
  • Dinh-Thuy Phan-Huy, Engineer at Orange Lab, Examiner
  • Guillaume Villemaud, Associate Professeur at INSA-Lyon, Thesis director
  • Florin-Doru Hutu, Associate Professor at INSA-Lyon, Co-director

PhD Defence: “Privacy in learning systems for healthcare”, Théo Jourdan, Amphitheater, Library UCBL, 29th of October 2021 at 9:00 PM

The defense will be held in the Amphitheater, Chappe Library UCBL, and will be streamed live here.

 

Title

Privacy in learning systems for healthcare

 

 

Abstract

With the development of the Internet of Things (IoT), smartphones and sensors are now able to provide information about the user’s activity and even their physiology. This has led to a growing interest from the scientific community, particularly in the field of e-health, with applications in the monitoring of patients undergoing rehabilitation in order to offer more personalized follow-up. However, in addition to guiding the rehabilitation process, the generation and transmission of IoT data is also vulnerable to privacy breaches. Indeed, the complex processing chain of the IoT application in healthcare multiplies the risk of privacy threats throughout the life cycle of IoT data, including collection, transmission and storage, by an adversary who can retrieve the data and re-identify or reveal sensitive patient information. This thesis focuses on the following questions: Is the data collected sufficiently protected so that no one can misuse it to re-identify the owner or infer sensitive information? Is the protected data still accurate enough for healthcare applications such as rehabilitation? Achieving balance between data utility and privacy protection is an important challenge that we explore in this thesis from different angles. More specifically, the first part focuses on the problem of data anonymization through minimization, while the second part focuses on preventing the inference of sensitive attributes through a Generative Adversarial Networks (GAN) to sanitize sensor data and an approach exploiting private layers in Federated Learning (FL).

 

 

Jury

  • Fossati, Caroline Professeure des Universités, Institut Fresnel Rapporteure
  • Vincent, Emmanuel Directeur de Recherche, INRIA Nancy Rapporteur
  • Bellet, Aurélien Chargé de Recherche, INRIA Lille Examinateur
  • Ben Mokhtar, Sonia Directrice de Recherche, LIRIS Examinatrice
  • Dieterlen, Alain Professeur des Universités, IRIMAS Examinateur
  • Frindel, Carole Maître de Conférences, INSA Lyon Co-directrice de thèse
  • Boutet, Antoine Maître de Conférences, INSA Lyon Co-directeur de thèse

PhD Defence: “Expliquer et justifier les systèmes de décisions algorithmiques”, Clément Henin, Amphitheater, Chappe Building, 13th of October 2021 at 15:00 PM

The defense will be held in the Amphitheater, Chappe Building, and will be streamed live here.

 

Title

Expliquer et justifier les systèmes de décisions algorithmiques

 

 

Abstract

Les systèmes décisionnels fondés sur un traitement algorithmique sont déjà présents dans de nombreux domaines et leur utilisation devrait encore s’accroître. Pour certains types d’applications, l’opacité de ces systèmes peut être un frein, voire un obstacle rédhibitoire, à leur utilisation. Dans cette thèse, nous nous intéressons à la production d’explications et de justifications en « boîte noire », c’est-à-dire sans accès au code du système de décision. L’avantage de cette démarche est de fournir des résultats qui peuvent s’appliquer à de nombreux systèmes, indépendamment de leur mode de fonctionnement. Notre première contribution est un système d’explications interactif, permettant à l’utilisateur de contrôler les propriétés de l’explication qui lui est fournie afin d’obtenir la plus adaptée à sa situation. La deuxième contribution propose une approche novatrice pour contester et justifier les résultats d’un algorithme. Ces approches théoriques ont donné lieu au développement de deux outils : IBEX et Algocate. Ces résultats théoriques sont confrontés au terrain au travers d’études utilisateurs, dont un travail mené sur l’algorithme Score Cœur utilisé pour l’attribution des greffons cardiaques. Cette étude combine des éléments sociologiques notamment sur l’appropriation par les acteurs de ce système de décision et le développement d’un outil d’explication et de justification adapté aux spécificités de Score Coeur.

 

 

Jury

  • Solnon, Christine, Professeure, INSA Lyon,
  • Amer-Yahia, Sihem, Directrice de recherche, LIG, Rapporteure
  • Gambs, Sébastien, Professeur, UQAM, Rapporteur
  • Jacquelinet, Christian, Médecin Spécialiste, Agence de la Biomédecine, Examinateur
  • Mabi, Clément, Maître de conférence, UTC Compiègne, Examinateur
  • Le Métayer, Daniel, Directeur de recherche, Inria, Co-Directeur de thèse
  • Castelluccia, Claude, Directeur de recherche, Inria, Co-directeur de thèse

PhD Defence: “Contribution à l’analyse des systèmes de communications pour le régime paquets courts”, Dadja Anade, Amphitheater, Chappe Building, 07th of October 2021 at 14:00 PM

The defense will be held in the Amphitheater, Chappe Building, and will be streamed live here.

 

Title

Contribution à l’analyse des systèmes de communications pour le régime paquets courts

 

 

Abstract

Dans cette thèse, une fonction qui approxime la fonction de répartition d’une somme de vecteurs aléatoires indépendants et identiquement distribués est présentée. L’erreur d’approximation est majorée, et par consequent, une borne supérieure et une borne inférieure sur la fonction de répartition sont obtenues. Pour des vecteurs aléatoires absolument continues ou discrètes regulières (“lattices”), l’approximation proposée est identique à l’approximation du point de selle de la fonction de répartition. Ce résultat est ulitisé pour approcher les bornes de probabilité d’erreur de décodage pour les canaux point à point et à accès multiple. Sur le canal point à point, cette approche a permis de constater l’insuffisance de l’approximation normale, particulièrement pour des probabilité d’erreur de décodage de faibles valeurs. Concernant les canaux à accès multiple, la considération de la notion d’erreur individuel a revélé le comportement presque non interférant des transmetteurs pour des petites valeurs de la probabilité d’erreur de décodage et de la longueur des paquets.

 

 

Jury

  • François BACCELLI – Directeur de recherche – INRIA – Rapporteur
  • H. Vincent POOR – Professeur – Princeton University – Examinateur
  • Aline ROUMY – Directeur de Recherche – INRIA – Examinateur
  • Michèle WIGGER – Professeur – Telecom ParisTech – Rapporteur
  • Samir M. Perlaza – HDR, Chargé de recherche – INRIA, Co-directeur
  • Philippe MARY – HDR, Maitre de conférence – Université de Lyon – Co-directeur
  • Jean-Marie Gorce – Professeur – Université de Lyon – Directeur de thèse

PhD Defence: “Gestion d’Interference Topologique pour les Réseaux Sans Fils Multi-utilisateurs”, Hassan Kallam, Amphitheater, Chappe Building, 28th of September 2021 at 14:00 PM

The defense will be held in the Amphitheater, Chappe Building.

 

Title

Gestion d’Interference Topologique pour les Réseaux Sans Fils Multi-utilisateurs

 

 

Abstract

La gestion d’interference topologique (de l’Anglais: Topological Interference Management – TIM) permet l’etude des dégrées de liberté (de l’Anglais: Degrees of Freedom – DoF) de réseaux sans fils soumis à l’interference partielle et dont la connaissance de l’état du canal est limitée seulement à la topologie du réseaux, autrement dit, les liens interférents faibles et forts. Dans ce manuscrit de thèse, nous considérons l’application de TIM pour les réseaux cellulaires d’une dimension (1D) linéaires et les réseaux cellulaires de deux dimensions (2D) hexagonales. Nous considérons le cas des utilisateurs uniformément distribués dans chaque cellule, ce qui donne une distribution continue d’utilisateurs. Ceci nous permet d’étudier la performance des classes d’utilisateurs au contraire des positions des utilisateurs individuels, comme a été fait auparavant. Nous considérons aussi la construction de la topologie au travers de l’analyse des seuils de l’interférence. Contrairement aux travaux existents nous utilisons TIM au niveau des classes des utilisateurs, ce qui nous permet de trouver la performance système en DoF indépendante de la position précise de chaque utilisateur. Ensuite, après avoir proposé un schéma de coloration fractionnaire des graphes resultants, pouvant atteindre la solution optimale de DoF, un compromis entre DoF et SIR est proposé. Cette thèse propose également une nouvelle approche pour construire une topologie d’interférence pour le problème TIM unicast des réseaux sans fil multi-utilisateurs. Fondée sur notre approche de construction de topologie d’interférence, nous pouvons évaluer la limite théorique des taux atteignables, dans le régime SNR asymptotique, pour le réseau sans fil sous-jacent et pas seulement pour sa représentation topologique d’interférence. Cette nouvelle approche nous permet de traiter le régime de SNR fini et pas seulement le régime SNR asymptotique avec l’analyse DoF. Un nouveau paramètre liée au seuil d’interférence, indépendant du SNR, est proposé et nous évaluons les débits symétriques réalisables du réseau sans fil, à la fois en régime SNR fini et en régime SNR asymptotique. Ensuite, nous présentons les bornes supérieures sur ce nouveau paramètre de seuil d’interférence normalisé pour les topologies d’interférence ayant une faisabilité en demi-DoF (de l’Anglais: Half-DoF-feasible), en considérant à la fois une allocation de ressources orthogonale et l’alignement d’interference (de l’Anglais: Interference Alignment – IA). Ces limites spécifient si une topologie d’interférence donnée realisable en demi-DoF peut être, en termes de taux réalisable, la meilleure topologie ou non. En utilisant ce résultat, nous limitons l’espace de recherche dans la plage de paramètres du seuil d’interférence normalisée, pour trouver des topologies d’interférence réalisables à demi-DoF ayant la possibilité d’être les meilleures topologies en termes de taux réalisable. Enfin, cette thèse considère une étude de cas sur le TIM pour les réseaux sans fil à petite échelle, dans laquelle, nous considérons le problème TIM pour les réseaux à quatre utilisateurs en employant notre approche de construction de topologie d’interférence proposée. Ensuite, nous appliquons l’analyse des débits réalisables, proposée dans le cadre de la nouvelle approche de construction de topologie d’interférence, pour toutes les topologies d’interférence réalisables à demi-DoF, à la fois par partage orthogonal et IA, dans le problème TIM de réseaux sans fil à quatre utilisateurs.

 

 

Jury

  • Florian KALTENBERGER – Maître de Conférences HDR – EURECOM – Rapporteur
  • Iñaki ESNAOLA – Senior lecturer – University of Sheffield – Rapporteur
  • Ghaya REKAYA-BEN OTHMAN – Professeur des Universités – TELECOM PARIS – Examinatrice
  • Laurent CLAVIER – Professeur des Universités – TELECOM LILLE – Examinateur
  • Leonardo S. Cardoso – Maître de Conférence – Université de Lyon – Co-directeur
  • Jean-Marie Gorce – Professeur des Universités – Université de Lyon – Directeur

PhD Defence: “Deep learning based approaches for detection in physical layer wireless multiple access”, Cyrille Morin, Amphitheater, Chappe Building, 22th of July 2021 at 14:00 PM

The defense will be held in the Amphitheater, Chappe Building, and will be streamed live here: link

 

Title

Deep learning based approaches for detection in physical layer wireless multiple access

 

 

Abstract

Current trends point towards an accelerated augmentation of devices with a desire to access the shared radio spectrum, both due to the continued democratisation and capability augmentation of user facing radio devices, such as cellphones, computers, and especially wearables, but also to the deployment of connected objects and sensors. Technology, protocols, and legislation improvements increase the available frequency bands by opening new channels in the GHz range, but the density of devices is nevertheless expected to increase. Multiple access to a shared radio frequency resource leads to situations that are both complex to model, and to tackle with known algorithms, and it is true of detection tasks that arise in the physical layer of a wireless transmission. The class of deep learning algorithms is especially useful in this sort of situation without model, or with non tractable algorithms, as long as a large amount of labelled data is available to train the related neural networks. This thesis aims at adapting the deep learning tool to physical layer detection problems, in successive steps of a decoding chain. First with the problem of detecting the origin of a received packet, starting with hardware fingerprinting of a transmitting device, and extending it to a scenario with multiple active devices at the same time, detecting the set of active devices transmitting an explicit codeword. The next step after origin detection is bit detection, to decode transmitted messages. For that, deep learning is used to learn constellations allowing for an efficient bit detection in a multiple-access scenario, namely the two-user uplink NOMA. Data used to train the networks involved in this thesis are gathered both from simulated models, and from experimental implementations in the FIT/CorteXlab software defined radio test-bed.

 

 

Jury

  • Marco Di Renzo – Research Director – CentraleSupélec – Reviewer
    Symeon Chatzinotas – Professor – University of Luxembourg – Reviewer
    Marwa Chafii – Associate Professor – ENSEA – Examiner
    Catherine Douillard – Professor – IMT Atlantique – Examiner
    Christophe Moy – Professor – Université de Rennes 1 – President
    Leonardo S. Cardoso – Associate Professor – Université de Lyon – Co-supervisor
    Jakob Hoydis – Principal Research Scientist – Nvidia – Co-supervisor
    Jean-Marie Gorce – Professor – Université de Lyon – Supervisor