2.1 Motivation

During the last century, the industry of communications was devoted to improving human connectivity, leading to a seamless worldwide coverage to cope with increasing data rate demands and mobility requirements. The Internet revolution drew on a robust and efficient multi-layer architecture ensuring end-to-end services. In a classical network architecture, the different protocol layers are compartmentalized and cannot easily interact. For instance, source coding is performed at the application layer while channel coding is performed at the physical (PHY) layer. This multi-layer architecture blocked any attempt to exploit low level cooperation mechanisms such as relaying, phy-layer network coding or joint estimation. During the last decade, a major shift, often referred to as the Internet of Things (IoT), was initiated toward a machine-to-machine (M2M) communication paradigm, which is in sharp contrast with classical centralized network architectures. The IoT enables machine-based services exploiting a massive quantity of data virtually spread over a complex, redundant and distributed architecture.

This new paradigm makes the aforementioned classical network architecture based on a centralized approach out-of-date.

The era of Internet of Everything deeply modifies the paradigm of communication systems. They have to transmute into reactive and adaptive intelligent systems, under stringent QoS constraints (latency, reliability) where the networking service is intertwined in an information-centric network. The associated challenges are linked to the intimate connections between communication, computation, control and storage. Actors, nodes or agents in a network can be viewed as forming a distributed system of computations—a computing network .

2.2 Scientific methodology

It is worth noting that working on these new architectures can be tackled from different perspectives, e.g. data management, protocol design, middleware, algorithmic design... Our main objective in Maracas is to address this problem from a communication theory perspective. Our background in communication theory includes information theory, estimation theory, learning and signal processing. Our strategy relies on three fundamental and complementary research axes:

• Mathematical modeling: information theory is a powerful framework suitable to evaluate the limits of complex systems and relies on probability theory. We will explore new bounds for complex networks (multi-objective optimization, large scale, complex channels,...) in association with other tools (stochastic geometry, queuing theory, learning,...)
• Algorithmic design: a number of theoretical results obtained in communication theory, despite their high potential are still far from a practical use. We will thus work on exploiting new algorithmic techniques. Back and forth efforts between theory and practice is necessary to identify the most promising opportunities. The key elements are related to the exploitation of feedbacks, signaling and decentralized decisions. Machine learning algorithms will be explored.
• Experimentation and cross-layer approach: theoretical results and simulation are not enough to provide proofs of concept. We will continue to put efforts on experimental works either on our own (e.g. FIT/CorteXlab and SILECS) or in collaboration with industries (Nokia, Orange, Thalès,...) and other research groups.

While our expertise is mostly related to the optimization of wireless networks from a communication perspective, the project of Maracas is to broaden our scope in the context of Computing Networks, where a challenging issue is to optimize jointly architectures and applications, and to break the classical network/data processing separation. This will drive us to change our initial positioning and to really think in terms of information-centric networks following, e.g. 48, 46, 53.

To summarize, Computing Networks can be described as highly distributed and dynamic systems, where information streams consist in a huge number of transient data flows from a huge number of nodes (sensors, routers, actuators, etc...) with computing capabilities at the nodes. These Computing Networks are nothing but the invisible nonetheless necessary skeleton of cloud and fog-computing based services.

Our research strategy is to describe these Computing Networks as complex large scale systems in an information theory framework, but in association with other tools, such as stochastic geometry, stochastic network calculus, game theory 23 or machine learning.

The multi-user communication capability is a central feature, to be tackled in association with other concepts and to assess a large variety of constraints related to the data (storage, secrecy,...) or related to the network (energy, self-healing,...).

The information theory literature or more generally the communication theory literature is rich of appealing techniques dedicated to efficient multi-user communications: e.g. physical layer network coding, amplify-and-forward, full-duplexing, coded caching at the edge, superposition coding. But despite their promising performance, none of these technologies play a central role in current protocols. The reasons are two-fold : i) these techniques are usually studied in an oversimplified theoretical framework which neglect many practical aspects (feedback, quantization,...), and that is not able to tackle large scale networks and ii) the proposed algorithms are of a high complexity and are not compatible with the classical multi-layer network architecture.

Maracas addresses these questions, leveraging on its past outstanding experience from wireless network design.

The aim of Maracas is to push from theory to practice a fully cross-layer design of Computing Networks , based on multi-user communication principles relying mostly on information theory, signal processing, estimation theory, game theory and optimization. We refer to all these tools under the umbrella of communication theory .

As such, Maracas project goes much beyond wireless networks. The Computing Networks paradigm applies to a wide variety or architectures including wired networks, smart grids, nanotechnology based networks. One Maracas research axis will be devoted to the identification of new research topics or scenarios where our algorithms and mathematical models could be useful.

3.1 General description

As presented in the first section, Computing Networks is a concept generalizing the study of multi-user systems under the communication perspective. This problematic is partly addressed in the aforementioned references. Optimizing Computing Networks relies on exploiting simultaneously multi-user communication capabilities, in the one hand, and storage and computing resources in the other hand. Such optimization needs to cope with various constraints such as energy efficiency or energy harvesting, delays, reliability or network load.

The notion of reliability (used in MARACAS acronym) is central when considered in the most general sense : ultimately, the reliability of a Computing Network measures its capability to perform its intended role under some confidence interval. Figure 1 represents the most important performance criteria to be considered to achieve reliable communications. These metrics fit with those considered in 5G and beyond technologies 50.

On the theoretical side, multi-user information theory is a keystone element. It is worth noting that classical information theory focuses on the power-bandwith tradeoff usually referred as Energy Efficiency-Spectral Efficiency (EE-SE) tradeoff (green arrow on 1). However, the other constraints can be efficiently introduced by using a non-asymptotic formulation of the fundamental limits 49, 51 and in association with other tools devoted to the analysis of random processes (queuing theory, ...).

Maracas aims at studying Computing Networks from a communication point of view, using the foundations of information theory in association with other theoretical tools related to estimation theory and probability theory.

In particular, Maracas combines techniques from communication and information theory with statistical signal processing, control theory, and game theory. Wireless networks is the emblematic application for Maracas, but other scenarios are appealing for us, such as molecular communications, smart grids or smart buildings.

Several teams at Inria are addressing computing networks, but working on this problem with an emphasis on communication aspects is unique within Inria.

This figure illustrates the four most important metrics allowing to evaluate wireless communication systems

The complexity of Computing Networks comes first from the high dimensionality of the problem: i) thousands of nodes, each with up to tens setting parameters and ii) tens variable objective functions to be minimized/maximized.

In addition, the necessary decentralization of the decision process, the non stationary behavior of the network itself (mobility, ON/OFF Switching) and of the data flows, and the necessary reduction of costly feedback and signaling (channel estimation, topology discovering, medium access policies...) are additional features that increase the problem complexity.

The original positioning of Maracas holds in his capability to address three complementary challenges :

1. to develop a sound mathematical framework inspired by information theory.
2. to design algorithms, achieving performance close to these limits.
3. to test and validate these algorithms on experimental testbeds.

3.2 Research program

This figure illustrates the Maracas organization around 4 research axes

Our research is organized in 4 research axes:

• Axis 1 - Fundamental Limits of Reliable Communication Systems: Information theory is revisited to integrate reliability in the wide sense. The non-asymptotic theory which made progress recently and attracted a lot of interest in the information theory community is a good starting point. But for addressing computing network in a wide sense, it is necessary to go back to the foundation of communication theory and to derive new results, e.g. for non Gaussian channels 10 of for multi-constrained systems 22.

  This also means revisiting the fundamental estimation-detection problem 52 in a general multi-criteria, multi-user framework to derive tractable and meaningful bounds.

  As mentioned in the introduction, Computing Networks also relies on a data-centric vision, where transmission, storage and processing are jointly optimized. The strategy of caching at the edge45 proposed for cellular networks shows the high potential of considering simultaneously data and network properties. Maracas is willing to extend his skills on source coding aspects to tackle with a data-oriented modeling of Computing Networks.

• Axis 2 - Algorithms and protocols: Our second objective is to elaborate new algorithms and protocols able to achieve or at least to approach the aforementioned fundamental limits. While the exploration of fundamental limits is helpful to determine the most promising strategies (e.g. relaying, cooperation, interference alignment) to increase system performance, the transformation of these degrees of freedom into real protocols is a non trivial issue. One reason is the exponentially growing complexity of multi-user communication strategies, with the number of users, due to the necessity of some coordination, feedback and signaling. The general problem is a decentralized and dynamic multi-agents multi-criteria optimization problem and the general formulation is a non-linear and non-convex large scale problem.

  The conventional research direction aims at reducing the complexity by relaxing some constraints or by reducing the number of degrees of freedom. For instance, topology interference management is a seducing model used to reduce feedback needs in decentralized wireless networks leading to original and efficient algorithms 54, 47.

  Another emerging research direction relies on using machine learning techniques 44 as a natural evolution of cognitive radio based approaches. Machine learning in the wide sense is not new in radio networks, but the most important works in the past were devoted to reinforcement learning approaches. The use of deep learning (DL) is much more recent, with two important issues : i) identifying the right problems that really need DL algorithms and ii) providing extensive data sets from simulation and real experiments. Our group started to work on this topic in association with Nokia in the joint research lab. As we are not currently expert in deep learning, our primary objective is to identify the strategic problems and to collaborate in the future with Inria experts in DL, and in the long term to contribute not only to the application of these techniques, but also to improve their design according to the constraints of computing networks.

• Axis 3 - Experimental validation : With the rapid evolution of network technologies, and their increasing complexity, experimental validation is necessary for two reasons: to get data, and to validate new algorithms on real systems.

  Maracas activity leverages on the FIT/CorteXlab platform (), and our strong partnerships with leading industry including Nokia Bell Labs, Orange labs, Sigfox or Sequans. Beyond the platform itself which offers a worldwide unique and remotely accessible testbed , Maracas also develops original experimentations exploiting the reproducibility, the remote accessibility, and the deployment facilities to produce original results at the interface of academic and industrial research 2, 13. FIT/CorteXlab uses the GNU Radio environment to evaluate new multi-user communication systems.

  Our experimental work is developed in collaboration with other Inria teams especially in the Rhone-Alpes centre but also in the context of the future SILECS project which will implement the convergence between FIT and Grid'5000 infrastructures in France, in cooperation with European partners and infrastructures. SILECS is a unique framework which will allow us to test our algorithms, to generate data, as required to develop a data-centric approach for computing networks.

  Last but not least, software radio technologies are leaving the confidentiality of research laboratories and are made available to a wide public market with cheap (few euros) programmable equipment, allowing to setup non standard radio systems. The existence of home-made and non official radio systems with legacy ones could prejudice the deployment of Internet of things. Developing efficient algorithms able to detect, analyse and control the spectrum usage is an important issue. Our research on FIT/CorteXlab will contribute to this know-how.

• Axis 4 - Other application fields : Even if the wireless network context is still challenging and provides interesting problems, Maracas targets to broaden its exploratory playground from an application perspective. We are looking for new communication systems, or simply other multi-user decentralized systems, for which the theory developed in the context of wireless networks can be useful. Basically, Maracas might address any problem where multi-agents are trying to optimize their common behavior and where the communication performance is critical (e.g. vehicular communications, multi-robots systems, cyberphysical systems). Following this objective, we already studied the problem of missing data recovery in smart grids 14 and the original paradigm of molecular communications 8.

  Of course, the objective of this axis is not to address random topics but to exploit our scientific background on new problems, in collaboration with other academic teams or industry. This is a winning strategy to develop new partnerships, in collaboration with other Inria teams.

4.1 5G, 6G, and beyond

The fifth generation (5G) broadens the usage of cellular networks but requires new features, typically very high rates, high reliability, ultra low latency, for immersive applications, tactile internet, M2M communications.

From the technical side, new elements such as millimeter waves, massive MIMO, massive access are under evaluation. The initial 5G standard validated in 2019, is finally not really disruptive with respect to the 4G and the clear breakthrough is not there yet. The ideal network architecture for billions of devices in the general context of Internet of Things, is not well established and the debate still exists between several proposals such as NB-IoT, Sigfox, Lora. We are developing a deep understanding of these techniques, in collaboration with major actors (Orange Labs, Nokia Bell Labs, Sequans, Sigfox) and we want to be able to evaluate, to compare and to propose evolutions of these standards with an independent point of view.

This is why we are interested in developing partnerships with major industries, access providers but also with service providers to position our research in a joint optimization of the network infrastructure and the data services, from a theoretical perspective as well as from experimentation.

4.2 Energy sustainability

The energy footprint and from a more general perspective, the sustainability of wireless cellular networks and wireless connectivity is somehow questionable.

We develop our models and analysis with a careful consideration of the energy footprint : sleeping modes, power adaptation, interference reduction, energy gathering, ... many techniques can be optimized to reduce the energetic impact of wireless connectivity. In a computing networks approach, considering simultaneously transmission, storage and computation constraints may help to reduce drastically the overall energy footprint.

4.3 Smart building, smart cities, smart environments

Smart environments rely on the deployment of many sensors and actuators allowing to create interactions between the twinned virtual and real worlds. These smart environments (e.g. smart building) are for us an ideal playground to develop new models based on information theory and estimation theory to optimize the network architecture including storage, transmission, computation at the right place.

Our work can be seen as the invisible side of cloud/edge computing. In collaboration with other teams expert in distributed computing or middleware (typically at CITIlab, with the team Dynamid of Frédéric Le Mouel) and in the framework of the chaire SPIE/ICS-INSA Lyon, we want to optimize the mechanisms associated to these technologies : in a multi-constrained approach, we want to design new distributed algorithms appropriate for large scale smart environments.

From a larger perspective we are interested on various applications where the communication aspects play an important role in multi-agent systems and target to process large sets of data. Our contribution to the development of TousAntiCovid falls into this area.

4.4 Machine learning based radio

During the first 6G wireless meeting which was held in Lapland, Finland in March 2019, machine learning (ML) was clearly identified as one of the most promising breakthroughs for future 6G wireless systems expected to be in use around 2030 (SNS 6G IA Horizon Europe). The research community is entirely leveraging the international ML tsunami. We strongly believe that the paradigm of wireless networks is moving toward to a new era. Our view is supported by the fact that artificial Intelligence (AI) in wireless communications is not new at all. The telecommunications industry has been seeking for 20 years to reduce the operational complexity of communication networks in order to simplify constraints and to reduce costs on deployments. This obviously relies on data-driven techniques allowing the network to self-tune its own parameters. Over the successive 3GPP standard releases, more and more sophisticated network control has been introduced. This has supported increasing flexibility and further self-optimization capabilities for radio resource management (RRM) as well as for network parameters optimization.

We target the following key elements :

• Obtaining data from experimental scenarios, at the lowest level (baseband I/Q signals) in multi-user scenarios (based upon FIT/CorteXlab).
• Developing a framework and algorithms for deep learning based radio.
• Developing new reinforcement learning techniques in high dimensional state-action spaces.
• Embedding NN structures on radio devices (FPGA or m-controllers) and in FIT/CorteXlab.
• Evaluating the gap between these algorithms and fundamental limits from information theory.
• Building an application scenario in a smart environment to experiment a fully cross-layer design (e.g. within a smart-building context, how could a set of object could learn their protocols efficiently ?)

4.5 Molecular communications

Many communication mechanisms are based on acoustic or electromagnetic propagation; however, the general theory of communication is much more widely applicable. One recent proposal is molecular communication, where information is encoded in the type, quantity, or time or release of molecules. This perspective has interesting implications for the understanding of biochemical processes and also chemical-based communication where other signaling schemes are not easy to use (e.g., in mines). Our work in this area focuses on two aspects: (i) the fundamental limits of communication (i.e., how much data can be transmitted within a given period of time); and (ii) signal processing strategies which can be implemented by circuits built from chemical reaction-diffusion systems.

A novel perspective introduced within our work is the incorporation of coexistence constraints. That is, we consider molecular communication in a crowded biochemical environment where communication should not impact pre-existing behavior of the environment. This has lead to new connections with communication subject to security constraints as well as the stability theory of stochastic chemical reaction-diffusion systems and systems of partial differential equations which provide deterministic approximations.

5.1 Footprint of research activities

Considering our research activities, most of our works are based on theoretical works or simulations. We may be concerned with the following aspects :

• Experimental works : To reduce the energy footprint of CorteXlab, all equipments are connected on Electronic Power Switches (EPS) with remote access. Then, the equipments can be turned on only when an experiment is launched.
• Computer sustainability : We use to keep the computers for at least 4 years, to avoid a fast turn-over.
• Travelling represents an important part of our CO2 footprint. For 2020, most of travels have been cancelled. In the future we believe that international events remain important for young researchers, but we will start a reflexion on this question.

5.2 Impact of research results

Our research may impact the energy consumption of the digital world even if the current debate on 5G is ill-posed. It is worth that the rebound effect associated to any technology should be thought carrefully.

Typially, the desing of former wireless protocols focused on high rates and high quality of service, with a lack of considering energy and CO${}_2$ footprint.

In the future, we will contribute to better understanding large scale impact of new communication technologies, and to investigate how innovation can help reducing the energy footprint, and may help to build a greener world.

6.1 Awards

Two PhDS have been defended :

• Mathieu Goutay developed ML based transmission chains. This is a joint work between Maracas and Nokia.
• Lélio Chetot developped new algorithms for MU detectors, using Gaussian approximate message passing theory and he integrated non homogeneous and correlated ativities in the model.

7.1 New software

7.2 New platforms

Participants: Pascal Girard, Jean-Marie Gorce, Mathieu Imbert, Cyrille Morin, Amaury Paris, Léonardo Sampaio Cardoso.

7.2.1 FIT/CorteXlab

FIT (Future Internet of Things) was a french Equipex (Équipement d'excellence) built to develop an experimental facility, a federated and competitive infrastructure with international visibility and a broad panel of customers. FIT is composed of four main parts: a Network Operations Center (FIT NOC), a set of IoT test-beds (FIT IoT-Lab), a set of wireless test-beds (FIT-Wireless) which includes the FIT/CorteXlab platform managed by Maracas team, and finally a set of Cloud test-beds (FIT-Cloud). In 2014 the construction of the room was done and SDR nodes have been installed in the room: 42 industrial PCs (Aplus Nuvo-3000E/P), 22 NI radio boards (usrp ) and 18 Nutaq boards (PicoSDR, 2x2 and 4X4) can be programmed remotely, from internet now.

As the FIT project development phase ended in 2019 , CorteXlab has seen continued usage as well as further developments. FIT/CorteXlab has been used by both INSA and the European GNU Radio Days (Gnu radio days) for both lectures and tutorials. Several scientific measurements campaigns have taken place in the FIT/CorteXlab experimentation room and are under works at the moment.

photography showing the CorteXlab plateform

In 2022 the following work has bee done:

• The usage of docker containers as the unit of experimentation has been further developed, with the addition and maintenance of new reference docker images, with the improvement of the overall system reliability, and with the improvement of the feedback provided to users through detailed logs collected during experiment runs.
• Automatic shutdown of unused nodes has been added, providing several benefits such as reduction of power consumption (both of the nodes and also the climate control, because the platforms produces less heat), and increase of the lifetime of experimental equipments (especially SDR nodes).
• The web user interface was greatly developed during 2020 (went into production January 2021) as the first web interface elements of several to come to help ease the usage of CorteXlab and lower the entry ticket to the platform. This web interface allows for users to manage their own accounts, freeing engineer time to other more important developmental tasks, as well as increasing the security to social hacker attacks. It provides a user friendly graphical interface so that users can book the platform and manage their reservations It also allows administrators to import several user accounts at the same time and to request revalidation of user accounts.
• Repair of the remotelly controlled Electric Power Switches (EPS). The EPSs are essential elements of the CorteXlab platform as it allows for remote control of the nodes' computers as well as maintenance. Many EPSs have experienced failure in the years since CorteXlab's inauguration. This hindered the full usage of the testbed. They have been fixed by the platform engineers and have had their life-time extended. However, a more realiable solution for electrically controlling the nodes must be sought.
• A reservation tool has seen its development start in 2021 and was delivered in production early 2022. It allows fast and easy reservations of the CorteXlab resources, which will potentially increase the pool of potential CorteXlab users, as well as free engineer more time. Experiment monitoring tool and service aggregation portal are some functionalities developped in 2022.
• As the radio hardware in the CorteXlab experimentation room gets old and outdated, its replacement by newer and more capable radios has become an important step to maintain CorteXlab a state of the art platform. These new USRPs will enable to experiment with demanding 5G systems as well as more refined measurement capabilities due to its high bandwidth. It will be paramount for new Deep Learning based experiments, such as radio localization and identification.
• The develoment of a LoRa basis for experimentation in CorteXlab has also seen the light of day during 2021 (described above as an independent software: Cortexlab_LORA_PHY). A new LoRa starting point for experimenting has been created, based from an existing EPFL implementation, with the adition of dynamic features as well as a simplified MAC layer. This was the work of both an Inria engineer as well as an intern. A deliverable from this project is a tutorial, hosted at the CorteXlab wiki page (tutorial Lora on CorteXlab) and presented in 43.
• The framework CorteXlab-IoT Framework has been renewed and transformed in a more flexible framework by Amaury Paris, to foster the use of multi-user access tests by other teams. This framework has been presented at the international GNU radio conference () and at the workshop on AFF3CT organized by Inria Bordeaux (.
• The construction of a dataset for radio signal detection and identification has been built in collaboration with L2S, Supelec.

In the coming years, we will pursue the following objectives for CorteXlab:

1. Continued update of old and rarely used radio hardware and replace it with new, more useful, and updated hardware. While the USRPs N2932 are still good options for many kinds of experiments, they present certain limitations with respect to frequency ranges, bandwidth and communication speeds with the computer interface, meaning that they are becoming less and less relevant as time passes. The PicoSDRs have not presented a great usage profile since they are hard to use, in either pure GNU Radio mode as well as FPGA mode. They demand a great investment of tools and time from the user side and are less well documented and supported by the manufacturer. The ideal scenario would be to replace all PicoSDRs with dual transceiver USRPs N2944R and co-equip all USRP N2932 nodes with dual channel N2900 (B210 equivalents) to offer more flexibility to the users and allow for other SDR systems, like Eurecom’s Openair Interface, to be easily deployed in CorteXlab.
2. Update all controlling node PCs with new and more capable machines, able to increase the complexity of the GNU Radio chains running in these machines. With the rise in popularity of Deep Learning systems for radio, some of these machines could also be equipped with a GPU unit to aid with in loco — on line Deep Learning systems.
3. Update the support equipment, increasing the networking bandwidth between the nodes themselves and also the server, which would allow for more relevant distributed (auto-coordination, distributed learning, etc…) and/or centralized (Deep Learning aggregation, cloud RAN, etc…) techniques to be studied.
4. The massive multi-user scenarios, one of the main targets of CorteXlab, are still a challenging problem do deal with both from the theoretical and experimentation fronts. We aim at providing several tools to ease with the experimentation of such scenarios, such as a) ready-made, usable multi-user frameworks with pluggable parts that allow for fast experimentation; b) extensive datasets as well as the tools used to create them, for users working with Deep Learning; c) more realistic (and close to standard compliance) physical layer systems, that will either be created from scratch or adapted from open source systems, will allow users to get closer to real life systems while remaining in the stable environment of CorteXlab’s experimentation room.
5. Nodes outside the experimentation room for real-life, real interference profile communications tests in the ISM band (and other bands we might be able to get licensing deals), under regulated norms. This objective requires tests to restrain the nodes’ transmission frequencies and powers to the ones allowed as to avoid harmful interference to other systems.

CorteXlab will be part of the SLICES/FR node of the European project SLICES, and will be integrated in the set of plateform for 5G and beyond in the framework of the PEPR 5G. We also expect to collaborate in the future SNS-IA program of Horizon Europe. More specifically, CorteXlab can be used to explore machine learning based radio, resource management and also tu evaluate joint sensing and communication concepts for twinning real and virtual worlds.

Fundamental limits:

Many applied problems in communication can be evaluated under the light of applied probabilities, at the root of information theory and estimation theory. A part of our work in axis 1 contributes in this area. In 2022 we obtained 4 important results:

• The fundamental limits in the finite block-length (FBL) have been used to evaluate joint channel coding of consecutive messages with heterogeneous decoding deadlines 36, 42.
• Optimal strategies to superpose URLLC and eMBB traffics has been established 37, 29.
• In a different setting, we evaluated physical layer security (PLS) by considering correlated fading channels and side information at the transmitter, exploiting the copula technique 30.
• We also considered a large scale scenario with random spatial fields of interferers over multiple carriers. We derived a novel tractable characterization of the interference probability distribution based on an application of Sklar’s theorem to develop a combination of $\alpha$-stable and t-copula dependence models 31

Algorithms in multi-user environments:

Multi-user network scenarios constitute the natural playground for our research, for instance considering an isolated cell in the downlink, or a LORA random access topology, etc... For each scenario, we may explore it under three perspectives: fundamental limits (axis 1), efficient algorithms (axis 2) and experimental evaluation (axis 3). Before going deeper in these contributions, we herein summarize the scenario studied this year

• Point-to-point transmissions: despite its long history and well-established results, the basic scenario made of one transmitter and one receiver still represents challenges: Typically, to adapt the transmission to new propagation channels (e.g. THz, VLC) with non classical properties, but also to support new QoS requirements (URLLC), which occurs especially in the context of machine to machine communications. Last but not least, revisiting the P2P communication problem under the light of machine learning may bring new breakthrough in terms of encoder/decoder complexity and adaptability.

  Our contributions this year concern first the evaluation of the optimal receiver in the context of an impulsive noise, which occur in random access large scale networks when the interference is considered as noise. We evaluated a new technique for log-likelihood ratio (LLR) estimation. This is an important output of the ANR Arburst, prepared in collaboration with INSA Rennes and IEMN Lille 33.

  In the PhD of Mathieu Goutay, we worked with Nokia Bell Labs on the design of ML aided PHY layer to enhance the end-to-end performance, to suppress the need of pilots, and to optmize the tradeoff between rates and bandwidth 41

• Multiple access channel: in this setup, individual nodes randomly send packets to a unique BS. In the IoT context, one isolated cell may serve thousand of nodes with low transmission probability. The design of an optimal setting for such scenario requires to consider as a whole the PHY and the MAC layers either in the downlink or the uplink. In this area, we proposed 3 new contribution. In the first one, we collaborated with Nokia and with the university of Adelaide (Australia) to elaborate a MAC protocol for visible light communications 27. We also proposed in collaboration with IMT Lille and Aalborg University, a new random access strategy considering the acitivity correlation between the sensors activity 38. This question has been also in the core of the PHD of Lelio Chetot where the impact of this correlation has been considered to elaborate a new Hybride Gaussian Approximate Message Passing (HGAMP) algorithm for user detection and channel estimation problems in multi-user setting 40. Last but not least, we also enhanced the IoT framework developed for CorteXlab to foster the evaluation of multiple access techniques 43. We also explored communication learning strategies 35.
• NOMA in the Downlink: In the downlink, we explored the use of a graph matching technique to maximise the density of connected devices using multiple-resources NOMA 34.

Crossroad studies:

The objective of axis 4 is either to explore new setups not directly related to the wireless context, or to explore new techniques or models that could be useful for wireless. In the past, we started to evaluate ML techniques, which are now part of our classical tools. We also worked extensively on the molecular communication problem. This year the focus was on :

• Quantum algorithms: the fundamental question we are addressing is on the use of quantum algorithms to improve hard problems, such as the active users detection in the multiple access framework 32, 39. This is the objective of Idham Habibie' PhD.
• Smart building, industry4.0: an important aspect for industrial processes is to limit exposure to system malfunction. We proposed in 28 the development of new algorithms based on modifier adaptation and reinforcement learning, which efficiently manage the tradeoff between cost minimization and identification.

8.1 Axis 1: fundamental limits

8.2 Axis 2: algorithms

8.3 Axis 3: experimental assessment

8.4 Axis 4 : side roads exploration

In this open-mind research area, we developed two research axes.

9.1 Bilateral contracts with industry

Participants: Naveed Ahmad, Lelio Chetot, Fabrice Dupuis, Malcolm Egan, Jean-Marie Gorce, Claire Goursaud, Alix Jeannerot, Homa Nikbakht, Léonardo Sampaio Cardoso, Shanglin Yang.

We have currently the following partnerships

1. Inria-Nokia Bell Labs common lab (600k€) : we are involved in two research actions; Analytics and Network Information Theory. They cover the funding of two PhDs (Cyrille Morin and Lelio Chetot), a partial funding of another PhD (Alix Jeannerot) and 1 postdoc (Homa Nikbakht) for Maracas.
2. SPIE-ICS (1Meuros, 2017-2021) : The Insa-Spie IoT Chair IoT chair relies on the expertise of the CITI Lab. The skills developed within the different teams of the lab integrate study, modeling, conception and evaluation of technologies for communicating objects and dedicated network architectures. It deals with network, telecom and software matters as well as societal issues such as privacy. This project supported the postdoc of Naveed Ahmad supervised by Malcolm Egan. The SPIE-ICS / Insa Lyon chaire on IoT has been setup in 2017 by JM Gorce for the benefit of the CITIlab. JM Gorce was the head of this chair from 2016 to 2019. Frédéric Le Mouel is heading the chair since sept 2019. The remaining budget for Maracas corresponds to one postdoc (Naveed Ahmad).

9.2 Bilateral grants with industry

1. CIFRE Nokia Bell Labs (2019-2022): PhD of Mathieu Goutay.
2. CIFRE Nokia Bell Labs (2021-2024): PhD of Shashwat Mishra.
3. CIFRE Nokia Bell Labs (2022-2025): PhD of Shanglin Yang.
4. CIFRE Orange Labs (2022-2025): PhD of Fabrice Dupuis.

10.1 International initiatives

10.2 International research visitors

10.3 European initiatives

10.4 National initiatives

11.1 Promoting scientific activities

11.2 Teaching - Supervision - Juries

11.3 Popularization

12.1 Major publications

• 1 articleB. C.Bayram Cevdet Akdeniz, M.Malcolm Egan and B. Q.Bao Quoc Tang. Equilibrium Signaling: Molecular Communication Robust to Geometry Uncertainties.IEEE Transactions on Communications692February 2020, 752 - 765
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• 2 articleG. C.George C. Alexandropoulos, P.Paul Ferrand, J.-M.Jean-Marie Gorce and C. B.Constantinos B. Papadias. Advanced coordinated beamforming for the downlink of future LTE cellular networks.IEEE Communications Magazine547Arxiv: 16 pages, 6 figures, accepted to IEEE Communications MagazineJuly 2016, 54 - 60
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• 3 articleS.Selma Belhadj Amor, S.Samir Perlaza, I.Ioannis Krikidis and H. V.H. Vincent Poor. Feedback Enhances Simultaneous Wireless Information and Energy Transmission in Multiple Access Channels.IEEE Transactions on Information Theory638August 2017, 5244 - 5265
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• 4 articleM.Mauro De Freitas, M.Malcolm Egan, L.Laurent Clavier, A.Alban Goupil, G. W.Gareth W. Peters and N.Nourddine Azzaoui. Capacity Bounds for Additive Symmetric $$ -Stable Noise Channels.IEEE Transactions on Information Theory638August 2017, 5115-5123
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• 5 articleM.Malcolm Egan, B. C.Bayram Cevdet Akdeniz and B. Q.Bao Quoc Tang. Equilibrium Signaling in Spatially Inhomogeneous Diffusion and External Forces.IEEE Transactions on Molecular, Biological and Multi-Scale Communications72June 2021, 106 - 110
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• 6 articleM.Malcolm Egan, L.Laurent Clavier, C.Ce Zheng, M.Mauro De Freitas and J.-M.Jean-Marie Gorce. Dynamic Interference for Uplink SCMA in Large-Scale Wireless Networks without Coordination.EURASIP Journal on Wireless Communications and Networking20181August 2018, 1-14
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• 7 articleM.Malcolm Egan, J.Jan Drchal, J.Jan Mrkos and M.Michal Jakob. Towards Data-Driven On-Demand Transport.EAI Endorsed Transactions on Industrial Networks and Intelligent Systems514June 2018, 1-10
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• 8 articleM.Malcolm Egan, V.Valeria Loscrì, T. Q.Trung Q Duong and M. D.Marco Di Renzo. Strategies for Coexistence in Molecular Communication.IEEE Transactions on NanoBioscience181January 2019, 51-60
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• 9 articleM.Malcolm Egan, T. C.Trang C Mai, T. Q.Trung Q Duong and M.Marco Di Renzo. Coexistence in Molecular Communications.Nano Communication Networks16February 2018, 37-44
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• 10 inproceedingsM.Malcolm Egan, S.Samir Perlaza and V.Vyacheslav Kungurtsev. Capacity sensitivity in additive non-gaussian noise channels.2017 IEEE International Symposium on Information Theory (ISIT)IEEE2017, 416--420
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• 11 articleI.Iñaki Esnaola, S.Samir Perlaza, H. V.H. Vincent Poor and O.Oliver Kosut. Maximum Distortion Attacks in Electricity Grids.IEEE Transactions on Smart Grid742016, 2007-2015
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• 12 articleY.Yasser Fadlallah, O.Othmane Oubejja, S.Sarah Kamel, P.Philippe Ciblat, M.Michele Wigger and J.-M. S.Jean-Marie S Gorce. Cache-Aided Polar Coding: From Theory to Implementation.IEEE Journal on Selected Areas in Information TheoryNovember 2021, 1-17
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• 13 articleY.Yasser Fadlallah, A. M.Antonia M. Tulino, D.Dario Barone, G.Giuseppe Vettigli, J.Jaime Llorca and J.-M.Jean-Marie Gorce. Coding for Caching in 5G Networks.IEEE Communications Magazine552February 2017, 106 - 113
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• 14 articleC.Cristian Genes, I.Iñaki Esnaola, S.Samir Perlaza, L. F.Luis F Ochoa and D.Daniel Coca. Robust Recovery of Missing Data in Electricity Distribution Systems.IEEE Transactions on Smart Grid2018
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• 15 inproceedingsJ.-M.Jean-Marie Gorce, Y.Yasser Fadlallah, J.-M.Jean-Marc Kelif, H. V.H Vincent Poor and A.Azeddine Gati. Fundamental limits of a dense iot cell in the uplink.Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2017 15th International Symposium onIEEE2017, 1--6
• 16 articleC.Claire Goursaud and J.-M.Jean-Marie Gorce. Dedicated networks for IoT : PHY / MAC state of the art and challenges.EAI endorsed transactions on Internet of ThingsOctober 2015
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• 17 articleM.Mathieu Goutay, F. A.Fayçal Ait Aoudia, J.Jakob Hoydis and J.-M. S.Jean-Marie S Gorce. Machine Learning for MU-MIMO Receive Processing in OFDM Systems.IEEE Journal on Selected Areas in CommunicationsJune 2021
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• 18 articleA.Arturo Guizar, C.Claire Goursaud and J.-M.Jean-Marie Gorce. Performance of IR-UWB cross-layer ranging protocols under on-body channel models with body area networks.Annals of Telecommunications - annales des télécommunicationshttp://link.springer.com/article/10.1007/s12243-016-0500-4March 2016, 453–46
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• 19 articleN.Nizar Khalfet and S. M.Samir M. Perlaza. Simultaneous Information and Energy Transmission in the Two-User Gaussian Interference Channel.IEEE Journal on Selected Areas in Communications371January 2019, 156-170
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• 20 articleT. C.Trang C Mai, M.Malcolm Egan, T. Q.Trung Q Duong and M.Marco Di Renzo. Event Detection in Molecular Communication Networks with Anomalous Diffusion.IEEE Communications Letters216February 2017, 1249 - 1252
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• 21 articleY.Yuqi Mo, M.-T.Minh-Tien Do, C.Claire Goursaud and J.-M.Jean-Marie Gorce. Up-Link Capacity Derivation for Ultra-Narrow-Band IoT Wireless Networks.International Journal of Wireless Information Networks243June 2017, 300-316
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• 22 inproceedingsS.Samir Perlaza, A.Ali Tajer and H. V.H Vincent Poor. Simultaneous Energy and Information Transmission: A Finite Block-Length Analysis.IEEE International Workshop on Signal Processing Advances in Wireless Communications2018
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• 23 articleV.Victor Quintero, S.Samir Perlaza, I.Iñaki Esnaola and J.-M.Jean-Marie Gorce. Approximate Capacity Region of the Two-User Gaussian Interference Channel with Noisy Channel-Output Feedback.IEEE Transactions on Information Theory647Part of this work was presented at the IEEE International Workshop on Information Theory (ITW), Cambridge, United Kingdom, September 2016 and IEEE International Workshop on Information Theory (ITW), Jeju Island, Korea, October, 2015. Parts of this work appear in INRIA Technical Report Number 0456, 2015, and INRIA Research Report Number 8861.July 2018, 5326-5358
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• 24 articleV.Victor Quintero, S.Samir Perlaza, I.Iñaki Esnaola and J.-M.Jean-Marie Gorce. When Does Output Feedback Enlarge the Capacity of the Interference Channel?IEEE Transactions on Communications662Part of this work was presented at the 11th EAI International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM), Grenoble, France, May 30-Jun 1 2016September 2017, 615-628
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• 25 articleD.Dimitrios Tsilimantos, J.-M.Jean-Marie Gorce, K.Katia Jaffrès-Runser and H. V.H. Vincent Poor. Spectral and Energy Efficiency Trade-Offs in Cellular Networks.IEEE Transactions on Wireless Communications151January 2016, 54-66
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• 26 articleY.Yi Yu, L.Lina Mroueh, D.Diane Duchemin, C.Claire Goursaud, G.Guillaume Vivier, J.-M.Jean-Marie Gorce and M.Michel Terré. Adaptive Multi-Channels Allocation in LoRa Networks.IEEE Access82020, 214177-214189
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12.2 Publications of the year

12.3 Cited publications

• 44 articleS.Sebastian Dörner, S.Sebastian Cammerer, J.Jakob Hoydis and S.Stephan ten Brink. Deep learning based communication over the air.IEEE Journal of Selected Topics in Signal Processing1212018, 132--143
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• 45 article M. G.Mohammad G Khoshkholgh, K.Keivan Navaie, K. G.Kang G Shin, V.VCM Leung and H.Halim Yanikomeroglu. Caching or No Caching in Dense HetNets? arXiv preprint arXiv:1901.11068 2019
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• 46 articleS.Songze Li, M. A.Mohammad Ali Maddah-Ali, Q.Qian Yu and A. S.A Salman Avestimehr. A fundamental tradeoff between computation and communication in distributed computing.IEEE Transactions on Information Theory6412018, 109--128
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• 47 articleW.Wei Liu, S.Shuyang Xue, J.Jiandong Li and L.Lajos Hanzo. Topological Interference Management for Wireless Networks.IEEE Access62018, 76942--76955
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• 48 articleY.Yuyi Mao, C.Changsheng You, J.Jun Zhang, K.Kaibin Huang and K. B.Khaled B Letaief. A survey on mobile edge computing: The communication perspective.IEEE Communications Surveys & Tutorials1942017, 2322--2358
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• 49 articleY.Yury Polyanskiy, H. V.H Vincent Poor and S.Sergio Verdú. Channel coding rate in the finite blocklength regime.IEEE Transactions on Information Theory5652010, 2307
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• 50 articleJ.J. Sachs, L. A.L. A. A. Andersson, J.J. Araújo, C.C. Curescu, J.J. Lundsjö, G.G. Rune, E.E. Steinbach and G.G. Wikström. Adaptive 5G Low-Latency Communication for Tactile InternEt Services.Proceedings of the IEEE1072Feb 2019, 325-349
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• 51 articleV. Y.Vincent YF Tan. Asymptotic estimates in information theory with non-vanishing error probabilities.Foundations and Trends®112014, 1--184
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• 52 inproceedingsG.Gonzalo Vazquez-Vilar, A. G.Albert Guillen i Fabregas, T.Tobias Koch and A.Alejandro Lancho. Saddlepoint approximation of the error probability of binary hypothesis testing.2018 IEEE International Symposium on Information Theory (ISIT)IEEE2018, 2306--2310
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• 53 inproceedingsQ.Qifa Yan, S.Sheng Yang and M.Michele Wigger. Storage, computation, and communication: A fundamental tradeoff in distributed computing.2018 IEEE Information Theory Workshop (ITW)IEEE2018, 1--5
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• 54 articleX.Xinping Yi and G.Giuseppe Caire. Topological interference management with decoded message passing.IEEE Transactions on Information Theory6452018, 3842--3864
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