Seminar of Carles Antón-Haro (Telecommunications Technological Center of Catalonia) on September 27th at 9.30am

The next CITI seminar will take place on September 27th, at 9.30am in the margin of Yuqi PhD defense. This seminar entitled “Machine- and Deep-Learning for Beam Selection in Hybrid Analog Beamforming Architectures” will be presented by Carles Antón-Haro from Telecommunications Technological Center of Catalonia.

Titre : Machine- and Deep-Learning for Beam Selection in Hybrid Analog Beamforming Architectures

Abstract : This talk deals with the application of deep learning (DL) and machine learning (ML) techniques to beam selection problems in the uplink of a mmWave communication system. Specifically, we consider a hybrid beamforming architecture comprising an analog beamforming (ABF) network followed by a zero-forcing baseband processing block. The goal is to select the optimal configuration for the ABF network based on the estimated AoAs of the various user equipments. To that aim, we consider (i) two supervised machine-learning approaches: k-nearest neighbors (kNN) and support vector classifiers (SVC); and (ii) a feed-forward deep neural network: the multilayer perceptron (MLP). Computer simulations reveal that, for a well-designed codebook of analog beamformers, this task can be effectively accomplished by such data-driven schemes. Performance, in terms of sum-rate, is very close to that achievable via exhaustive search, in particular for the MLP.

Bio : Carles Antón-Haro received his PhD degree in Telecommunications Engineering from the Technical University of Catalonia in 1998 (cum-laude). He also holds a Master in Business Administration (MBA) from EADA Business School (2014, Best Final Degree Project Award). In the pursuit of his PhD degree, he was a recipient of scholarship granted by the Dept. of Universities and Research of the Generalitat de Catalunya (1995-1998). As a Research Assistant (1994-1998, UPC) and Research Associate (1998-1999, UPC), he participated in several EC Projects (Tsunami, Tsunami II, Sunbeam), as well as in other projects funded by the Spanish government. He was Teaching Assistant in the field of Computer Architecture (UPC 1994, UOC 1998-2012). In 1999, he joined Ericsson Spain, where he participated in two rollout projects of 2G and 3G mobile networks (2000, Regional Coordinator).

Currently, he is with the CTTC, where he works as a Director of R&D Programs and Senior Research Associate. Main duties, in addition to his daily research activity, include the overall coordination of CTTC’s participation in publicly-funded R&D projects and technology transfer projects, networking activities towards the establishment of strategic alliances with the industry and academia, monitoring of R&D funding programs and identification of new opportunities, and interaction with CTTC’s Scientific Committee in what concerns R&D programs. In the past, he was also in charge of the recruitment of human resources at the CTTC, and he defined the internal processes in relation with CTTC’s project portfolio. Since 2001, he has promoted or coordinated over 60 R&D projects or proposals and has been directly involved in some of them (P2P SmartTest, NEWCOM#, EXALTED, eCROPS, ADVANTAGE, WINNER, to name a few). He is an elected member of the Steering Board of the Networld2020 European Technology Platform since 2009 (formerly known as Net!works).

His research interests are in the field of signal processing for communications, this including radio interface design, multi-user MIMO, wireless sensor networks, opportunistic communications, link layer protocols (MAC, H-ARQ); and estimation theory with emphasis in state estimation for Smart Electricity Grids. He has published +20 technical papers in IEEE journals, books and book chapter; as well as +80 papers in international and national conferences. He is a recipient of the 2015 Best Paper Award of the Transmission, Access, and Optical Systems (TAOS) Technical Committee’s (Green Communications Track, ICC). He has supervised four Master Theses and five PhD Theses (two in progress). He has also acted as a reviewer of project proposals for various (inter)national funding agencies (e.g., ANEP, AGAUR, MIUR, ANR, ANVUR) and takes part in PhD Evaluation Committees on a regular basis.


CITI Talk: “Source coding under massive random access: theory and applications”, Aline Roumy (INRIA Rennes), Wednesday, September 12th at 11am in TD-C

TITLE: Source coding under massive random access: theory and applications.

Date – 12/09/18,11h-12h, TD-C.

In this presentation we will introduce a novel source coding problem allowing massive random access to large databases. Indeed, we consider a database that is so large that, to be stored on a single server, the data have to be compressed efficiently, meaning that the redundancy/correlation between the data have to be exploited. The dataset is then stored on a server and made available to users that maywant to access only a subset of the data. Such a request for a subset of the data is indeed random, since the choice of the subset is user-dependent. Finally, massive requests are made, meaning that, upon request, the server can only perform low complexity operations (such as bit extraction but no decompression/compression).
After describing the problem, information theoretical bounds of the source coding problem will be derived. Then two applications will be presented: Free-viewpoint Television (FTV) and massive requests to a database collecting data from a large-scale sensor network (such as Smart Cities).

Aline Roumy received the Engineering degree from Ecole Nationale Superieure de l’Electronique et de ses Applications (ENSEA), France in 1996, the Master degree in 1997 and the Ph.D. degree in 2000 from the University of Cergy-Pontoise, France. During 2000-2001, she was a research associate at Princeton University, Princeton, NJ. OnNovember 2001,she joined INRIA, Rennes, France as a research scientist. She has held visiting positions at Eurecom and Berkeley University. She serves as an Associate Editor for the Annals of telecommunications. Her current research and study interests include the area of statistical signal and image processing, coding theory and information theory. She is currently leading a project entitled Interactive Communication (InterCom) on Massive random access to subsets of compressed correlated data, and supported by the French Cominlabs excellence laboratory.

CITI Talk: “IF Neuron: theoretical study and application to digital communication”, Anne Savard (Associate Professor, IMT Lille), July 9th, at 10:30 am in TD-D room


IF Neuron: theoretical study and application to digital communication


In the context of digital communication, one main mechanism proposed in the literature to overcome the large consumption of MAC layers when establishing communications is called wake-up radio: The main processor is only waking up when receiving a specific signal, as for instance the node ID in the network. Unfortunately, since most of the wake-up receivers rely on standard micro-controller, they suffer a large decrease of energy efficiency. Nevertheless, if the wake-up receivers was designed with neuromorphic circuits, one could achieve high energy efficiency for IoT and ad hoc networks.

The main question that is tackled in this presentation is whether a neuro-inspired detection scheme using an Integrate-and-Fire neuron is reliable enough when one needs to detect a weak signal surrounded by noise.


Anne Savard received the Eng. degree in Electrical Engineering with specialization in Multimedia Systems from the Ecole Nationale Supérieure de l’Electronique et de ses Applications (ENSEA), Cergy-Pontoise, France, and the M.Sc. degree in Intelligent and Communicating Systems from Univeristé Cergy-Pontoise, both in 2012.

From October 2012to September 2015,she was a PhD student at ETIS Laboratory/ENSEA, under the supervision of Claudio Weidmann and David Declercq. Her research interests include modern channel coding, cooperative communication and multi-user information theory.

She defended her PhD entitled ‘Coding for cooperative communications: Topics in distributed source coding and relay channels’ on September, 22th, 2015.

CITI Talk: “Eye Tracking Algorithms”, Prof Radu Gabriel Bozomitu (“Gheorghe Asachi” Technical University, Romania), June 12th, at 11 am in “salle TD-C” ( Claude Chappe Building)


Eye Tracking Algorithms


In recent years, the interest in eye detection applications has increased considerably. There are a lot of eye detection methods used in different applications such as neuroscience, psychology, assistive technologies, in order to communicate with disabled patients, computer gaming, monitoring technologies for driver’s fatigue (in commercial and public transport), advertising industry, people identification based on face recognition and eye (iris) detection and in different military applications to help pilots to aim weapons just by looking at a target. A head-mounted eye tracking interface consists of an infrared video camera mounted on a frame glasses right underneath the eye, connected to a PC (or laptop), for eye pupil image acquisition and processing. This device is used to measure the point of gaze or the motion of an eye relative to the head. The presentation will focus on the software component used in eye tracking interfaces for real-time applications, which includes the algorithms for eye image binarization, pupil center detection, system calibration, mapping and ideogram selections. Different types of pupil detection algorithms are comparatively presented: the least squares fitting of ellipse, the RANdom SAmple Consensus (RANSAC) paradigm, the circular/elliptical Hough transform- based approaches, the projection method algorithm, the detection of the maximum dark area centroid in the eye image and the STARBURST algorithm.


Radu Gabriel Bozomitu received the degree in communications and electronic engineering; the master degree in the field of digital radio-communications; and the Ph.D. degree from the “Gheorghe Asachi” Technical University of Iaşi, Faculty of Electronics, Telecommunications and Information Technology in 1995, 1996 an  2005, respectively. R. G. Bozomitu obtained the PhD advisor position in 2017 and works as professor at the Department of Telecommunications and Information Technologies from Faculty of Electronics, Telecommunications and Information Technology from the “Gheorghe Asachi” Technical University of Iaşi. His present interests are in the areas of radio communications, analog integrated circuit design and assistive technology. Courses taught at “Gheorghe Asachi” Technical University of Iasi: “Radio communications“, “VLSI implementation of the radiofrequency circuits” and “Advanced radio communications”. He has edited or co-authored five books on analog VLSI circuits design, radiocommunications and assistive technology.

CITI Talk: “Maximising the Utility of Virtually Sliced Millimetre-Wave Backhauls via a Deep Learning Approach”, Rui Li, PhD student at the University of Edinburgh, Inria antenne


Maximising the Utility of Virtually Sliced Millimetre-Wave Backhauls via a Deep Learning Approach


Advances in network programmability enable operators to ‘slice’ the physical infrastructure into independent logical networks. By this approach, each network slice aims to accommodate the demands of increasingly diverse services. Precise allocation of resources to slices across future 5G millimetre-wave backhaul networks, so as to optimise their utility, is however challenging. This is because the performance of different services often depends on conflicting requirements, including bandwidth, sensitivity to delay, or the monetary value of the traffic incurred. In this talk, I will present our recent work in which we propose a general rate utility framework for slicing mm-wave backhaul links, which encompasses all known types of service utilities, i.e. logarithmic, sigmoid, polynomial, and linear. We then employ a deep learning solution to tackle the complexity of optimising non-convex objective functions built upon arbitrary combinations of such utilities. Specifically, using a stack of convolutional blocks, our approach can learn correlations between traffic demands and achievable optimal rate assignments. The proposed solution can be trained within minutes, following which it computes rate allocations that match those obtained with state-of-the-art global optimisation algorithms, yet orders of magnitude faster. This confirms applicability to highly dynamic traffic regimes and we demonstrate up to 62% network utility gains over a baseline greedy approach.

CITI Talk: “Recycler les ondes radio ambiantes pour connecter les objets”, Dinh-Thuy PHAN-HUY (Orange, Chatillon), 22 May (10h30 in TD-C)


Recycler les ondes radio ambiantes pour connecter les objets


o   Lors de l’édition 2017 du Salon de la recherche Orange, du 5 au 7 décembre (, Orange a réalisé, pour la première fois, une transmission de données sans fil,  effectuée grâce aux seules ondes déjà diffusées par… la tour Eiffel ! Aucune onde supplémentaire n’a été émise. Cette technologie dite de rétro-diffusion ambiente découverte par l’Université de Washington en 2013, a une sobriété énergétique exceptionnelle. Elle permet de fournir de nouveaux services sans dépenser plus en spectre et en puissance rayonnée, ouvre d’énormes possibilités en termes d’utilisation massive d’objets connectés pour les villes, les maisons et les usines intelligentes.

o   Aujourd’hui, pour la  première fois, le projet ANR SpatialModulation ( dirigé par Orange, tentera une démonstration en temps réel d’une communication utilisant les ondes TV de la Fourvière, entre un « émetteur » (qui n’émet pas) développé par Orange et un récepteur développé par l’Institut Langevin sur GNU Radio.

CITI Talk: “Optimization Algorithms for Solving Problems Arising from Large Scale Machine Learning”, Vyacheslav Kungurtsev (Czech Technical University, Prague), May 7th, at 2pm in “salle TD-C” ( Claude Chappe Building)


Optimization Algorithms for Solving Problems Arising from Large Scale Machine Learning


In the contemporary “big data” age, the use of Machine Learning models for analyzing large volumes of data has been instrumental in a lot of current technological development. These models necessitate solving very large scale optimization problems, presenting challenges in terms of developing appropriate solvers. In addition, especially for problems arising from Deep Neural Network architectures, the resulting problems are often nonconvex, and sometimes nonsmooth, giving additional difficulty.

In this talk I present the standard structural elements of this class of problems, and how these structures can be handled with appropriate parallel architectures. I discuss the state of the art in terms of optimization algorithms for this setting and summarize the prognosis for ongoing and future research.