5G networks are expected to revolution our living environments, our cities and our industry by connecting everything. 5G design has, thus, to meet the requirements of two “new” mobile services: massive Machine-Type Communications (mMTC), and Ultra Reliable Low Latency Communications (URLLC). Slicing concept facilitates serving these services with very heterogeneous requirements on a unique infrastructure. Indeed, slicing allows logically-isolated network partitioning with a slice representing a unit of programmable resources such as networking, computation and storage. Slicing was originally proposed for core networks, but is now being discussed for the Radio Access Network (RAN) owing to the evolution of technologies which now enable its implementation. These technologies include mainly the tendency for virtualizing the RAN equipment and its programmable control, the advent of Mobile Edge Computing (MEC) and the flexible design of 5G on the physical and MAC layers. However, the complete implementation of slicing in the RAN faces several challenges, in particular to manage the slices and associated control and data planes and for scheduling and resources allocation mechanisms. MAESTRO-5G project develops enablers for implementing and managing slices in the 5G radio access network, not only for the purpose of serving heterogeneous services, but also for dynamic sharing of infrastructure between operators. For this aim the project puts together exerts on performance evaluation, queuing theory, network economy, game theory and operations research. MAESTRO-5G is expected to provide: •A resource allocation framework for slices, integrating heterogeneous QoS requirements and spanning on multiple resources including radio, backhauling/fronthauling and processing resources in the RAN. •A complete slice management architecture including provisioning and re-optimization modules and their integration with NFV and SDN strata. •A business layer for slicing in 5G, enabling win-win situations between players from the telecommunications industry and the verticals, ensuring that the 5G services are commercially viable and gain acceptance in the market. •A demonstrator showing the practical feasibility as well as integration of the major functions and mechanisms proposed by the project, on a 5G Cloud RAN platform. The enhanced platform is expected to support the different 5G services (eMBB and IoT) and to demonstrate key aspects of slicing, such as: - Ability to create and operate in parallel multiple slices, on the same infrastructure and sharing the same radio resources (e.g. spectrum), each having different service requirements. - Ability to create and operate in parallel and independently different slices, sharing the same infrastructure/spectrum, belonging to different business actors, such as different operators. - Demonstrate inter-slice control ensuring respect of SLAs and a fair resource sharing.

Imagine designing and deploying a distributed application on millions of machines simply by posting a link on Twitter or by buying a word on Google Adwords. Imagine doing this without relying on a cloud or a central authority, just through a decentralized execution environment composed of users’ browsers that autonomously manages issues such as communication, naming, heterogeneity, and scalability. The introduction of browser-to-browser communication with WebRTC's Datachannel has made these scenarios closer, but today only experts can afford to tackle the technical challenges associated with large-scale browser-based deployments such as decentralized instant-messaging (Firechat) and Infrastructure-less Mission Critical Push To Talk. OBrowser aims to solve these challenges by means of a novel programming framework.

Mobile telecommunication networks and services are complex systems that are today planned and dimensioned by expert engineers in a static fashion, based on a limited set of local measurements and long-term statistics. In practice, however, the whole milieu is far from static, as subscribers are mobile by definition, and their communication activity patterns are strongly time-varying and location-dependent. In addition, the user traffic is increasingly generated by services that entail very different offered loads and requirements. The strong contextual and content heterogeneity that characterizes the mobile traffic demand makes the sheer increase of capacity an inefficient strategy towards next-generation mobile networks. The capacity growth must be coupled with a much more efficient usage of the resources: next-generation mobile networks are expected to be flexible enough to adapt themselves to spatiotemporal variations and content diversity, and to do so in a timely and automated manner. This results in so-called cognitive mobile networks. These networks will run big data analytics on traffic measurements from in-network monitoring probes, so as to extract important knowledge about the current status of the system. This knowledge will then become a fundamental input for automatic network functions to engineer traffic and allocate resources in concertation with the needs of end users. This vision primarily builds on the effective orchestration of network resources and services across all network levels. There is thus an ongoing substantial effort to define architectures for dynamic resource allocation, which all include orchestrator components in charge of taking automated decisions about resource reconfiguration. However, the dynamic resource management algorithms and policies that will run inside network orchestrators are to be entirely investigated and defined. Designing orchestrator policies and algorithms entails a number of scientific and technological challenges: (i) Which data analytics shall drive cognitive networks? (ii) How to ensure scalable and real-time orchestration? (iii) How to integrate analytics into current virtualized network architectures? and (iv) Which are the gains of cognitive management? To address the challenges, the CANCAN project targets the following objectives: 1) Collecting novel measurement datasets that describe mobile network data traffic at unprecedented spatial and temporal accuracy levels, and for different mobile services separately. The datasets will be gathered in an operational nationwide network. 2) Evaluating existing analytics for classification, prediction and anomaly detection within real-world high-detail per-service mobile network data, and tailoring them to the specifications of the management of resources at different network levels. 3) Demonstrating the integration of data analytics within next-generation cognitive network architectures in three practical case studies, i.e., (i) predictive and differentiated radio scheduling in virtualized Radio Access Network (vRAN), (ii) dynamic management of virtual machines and containers in Mobile Edge Computing (MEC), and (iii) dynamic Service Level Agreements and Class of Service generation for resource sharing and bottleneck mitigation. The integration will build on recent advances in architectures that enable Network Functions Virtualization. The CANCAN project brings together four partners: the networking and artificial intelligence teams of Thales Communications & Security (Thales) the Agora team of the Inria Rhone-Alpes center (Inria), the Computer Science laboratory (LIP6) of the Sorbonne University, and the SENSE department of Orange Labs (Orange). The consortium will leverage its expertise in mobile networks (Thales, Inria, LIP6), network data analytics (Thales, Inria, LIP6, Orange) and sociology (Orange) to achieve its proposed objectives.

The use of spectrum in millimeter bands is becoming essential to enable future wireless networks to offer significant capacity gains. However, as propagation losses become significant, antenna and beam formation become key elements in maintaining a reasonable range and limited infrastructure costs. Phased array antenna solutions require a very large number of RF chains and are expensive. The project aims to develop innovative alternative solutions based on reconfigurable metasurfaces. The work will focus on three areas of research: the practical implementation of such antennas in the EHF bands, technical and algorithmic solutions enabling the antennas to address several users simultaneously, and the rapid reconfiguration of the beams adapted to the radio channel. To reach the aforementioned objectives, the project will first precise scenarios and usages, highlight mmWave radio channel constraints, and provide recommendations on metasurface based antenna design. This will be performed in WP1 during the first 6 months. WP2 addresses the design of electronically steerable antennas based on an array of unit cells having the possibility to reflect/transmit impinging waves from the feeder(s) with a phase shift in a predefined set including zero phase shift. To ensure reconfigurability of these unit cells, a control system will be defined and implemented. Unit cells will be optimized so that their phase shift remains within a given percentage of its nominal values over the bandwidth of interest. Two prototypes will be implemented in WP2: first a full antenna system including an electronically controlled transmitarray, the multi-element focal systems and the digital control will be designed, optimized, fabricated and fully characterized by CEA Leti in the 26-28 GHz band. The antenna system will be able to generate at least four independent beams. The know-how of CEA Leti on transmitarrays is almost unique and has been mostly developed during a long collaboration with the IETR antenna team. Coding metasurface for cmWave and mmWave are currently developed in DOME group at IEMN. In the framework of a partnership with DGA, the main goal has been to propose artificial structures for Radar Cross Section reduction. In this project, as an alternative to absorbing layers previously studied in the group, the idea is to deviate the incident beam in one or several directions out of the detector spatial range. This approach can also be used to select the beam reflection direction. Regarding the size reduction inherent to frequency increase up to 60 GHz, an external company (INODESIGN) will be in charge of the fabrication. CSAM group of IEMN will bring its expertise in mmWave tune able structures using semi-conductor switching devices. The characterization of the reflectarray will be carried out at the telecom platform of IRCICA institute. To this aim, the 60 GHz anechoic chamber, already available at IRCICA, will be completed with a Newport monitored platform allowing an accurate angular control. In WP3, BF will be studied for both single and multi-user cases assuming possibly more than one stream per user. The optimization of the grid can be performed off line (using predefined codebooks) or dynamically by applying algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), or deep learning (DL) approaches. The CNAM and Orange will provide their expertise in that domain to design and implement these algorithms. Performance evaluation of the prototypes and BF algorithms will be performed in a real environment in WP4 with channel sounder, or an SDR approach. This WP will be led by Orange, with contributions of all the partners, to ensure an efficient exploitation of MESANGES results in 5G+ and 6G networks.

QUANTISELF is an interdisciplinary Collaborative Research Projects involving Enterprise (sociology, anthropology, information and communication science, ergonomics) that aims to analyse the role played by reflexive self-tracking technologies in order to quantify the Self or producing “Self knowledge through numbers” or Quantified Self (QS). Such devices measure, record and put together various personal data in such a way that shapes a digitized human body both through behavioral and physical activities data. It analyses the uses and design of these systems and aims to understand the actual practices and social issues in terms of appropriation and domestication of these technologies, “soft” regulation of behaviors (or “nudge”), and through the production of “computed” individuals. The QS produces a subject framed by thresholds which eventual crossing becomes a notable event, in ways shaped by viewing modes and displays. We assume that these systems produce a divided and particular way of experiencing the world in which the differences between ordinary experience (be it mobility, consumer and health) and data displayed by these devices happen not only to questioning users as well as directing them. The issues analysed by QUANTISELF therefore are as cognitive as normative In order to duly describe how such devices that put people’s bodies at the center of digital lives matter, this empirical research strives to articulate a study of the Quantified Self movement and designers of technologies with a grounded approach of users experiences and sense making process framed by individual and collective perspectives. With this aim in mind, QUANTISELF researchers articulate a large qualitative investigation about users (as many women as men) on one side and about designers and organizers of this social world on the other, with a quantitative survey dealing with both QS’ representations and practices. The investigation on uses is planned to be conducted in two waves (a year apart) and questions both the private and professional contexts of use, as well as the intertwining issues of wellness, physical and sports activities or health related at different stages of life. From the results obtained, a synthesis on “social challenges of the quantified self” will be written collaboratively by all the project partners. It will bring an original empirical and theoretical light on individual, social and organizational social issues raised by Big Data and the spread of new devices that shape the spread of personal digital devices in our society.. Following this interdisciplinary perspective, this project is articulated around three areas of research: 1) the analysis of the QS rise as a socio-political and technological original movement; 2) understand the individual experience of the people using QS tools: how does one act in a data reflexive eco-system in which technology happens to play a direct role (or not) in the reflexive making of action? And 3) a full account of the socialization forms of such experience and the place of self-quantification practices in the making and remaking of relationships and social ties. The collaborative research-Enterprise research project QUANTISELF is coordinated by the CERLIS laboratory of the University Paris Descartes and managed with Telecom ParisTech and Orange. It is expected to take place over a period of 36 months.