2023Activity reportProject-TeamMARACAS

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This new paradigm makes the aforementioned classical network architecture based on a centralized approach out-of-date.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. 58, 56, 63.

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 60.

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 59, 61 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 62 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 edge55 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 64, 57.

  Another emerging research direction relies on using machine learning techniques 54 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

• Maxime Guillaud is a Distinguished Lecturer of the IEEE Communications Society for the period 2023-2024.
• Mathieu Goutay got the best PhD award in the field of computer sciences at INSA Lyon (dec, 2023).
• The paper 46 co-authored by Maxime Guillaud received the best paper award at the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, Pittsburgh, PA (USA).

6.2 New European projects

• Maxime Guillaud is co-PI of the project CHASER (Chist-ERA program, call 2022). This project deals with the development of channel charting as a Service, and falls in the area of Joint Communication and Sensing.
• Maxime Guillaud and Jean-Marie Gorce are co-PIs of the INSTINCT project funded by Hoprizon Europe in ths SNS 6G-IA program. The project deals with the design of new waveforms for joint communication and sensing, and will also involve Cyrille Morin and Léonardo S. Cardoso .

6.3 National strategy

• Maracas is involved in 3 projects of the PEPR Networks of the Futu(PEPR-NF): the project PERSEUS on cell-free in sub-millimetric frequency range, the project FOUNDS on fundamental limits of networks and the project NF-Plateforme XG which develops the experimental platform for the PEPR. As such, this testbed is also part of the European SLICES ESFRI program.

6.4 Permanent positions filled

• In 2023, the group was happy to welcome a new senior researcher, Dr Maxime Guillaud . His activity covers massive access related topics, including tensor representations, as well as joint communication and sensing topics.
• A new permanent research engineer, Dr Cyrille Morin , expert in machine learning for radio systems and related software development.

7.1 New software

7.2 New platforms

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

7.2.1 FIT/CorteXlab toward integration in SLICES/Europe

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 2023, CorteXlab has been integrated in the roadmap of the PEPR-NF plateform project and will be funded for a renew of the full infrastructure, in coordination with Raymond Knopp from Eurecom, and Walid Dabbous from the team DIANA, Inria Sophia. The new deployment is expected by the end of 2024.

8.1 Random and massive access for IoT

Participants: Léonardo Cardoso, Lelio Chetot, Malcolm Egan, Jean-Marie Gorce, Claire Goursaud, Maxime Guillaud, Alix Jeannerot, Shashwat Mishra, Mateus Pontes Mota.

In the context of IoT, a critical problem is to be able to serve many users, having each a low probability of transmission. The problem appears in the downlink as well as in the uplink but the problems in terms of signaling are different. This research axis is lead by Jean-Marie Gorce , but all Maracas members have contributions around this scenario. This topic is covered in axes 1, 2 and 3. We present below the results of the year.

In 44, we propose a framework for maximizing the number of machine-type devices connected in the uplink of a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA). The system is based on the fast-uplink grant (FUG), where the base station (BS) schedules the access for active devices requesting connection. This problem is a mixedinteger non-convex problem and real-time solutions using general solvers are computationally prohibitive. The proposed scheduling solution comprises efficient device clustering and optimum power allocation using a bipartite graph matching approach, termed connection throughput maximizing full matching with pruning (CTMBM). Different from the other solutions of state-of-the-art, our proposed scheme considers scheduling over multiple transmission time intervals while considering the transmission deadlines and quality of service (QoS) for the devices. Additionally, we provide a method for priority scheduling of a subset of devices. We compare our solution to the state-of-the-art schemes and analyze the achieved gains through Monte-Carlo computer simulations.

An important part of the work elaborated with Lelio Chetot and Malcolm Egan , concerns the study of non independent and identical distributions of messages. We studied both transmission and reception strategies.

In 38, we studied the context of Industrial IoT (IIoT) as it is one of the major verticals targeted by the next generations of wireless networks. In order to provide industrial plants with features relying on wireless communications, the grant-free RA (GFRA) protocol appears to be a promising means for supporting massive ultra-reliable connectivity; at the same time, it is a critical bottleneck that requires an access point (AP) to be able to jointly perform active user detection and channel estimation (AUDaCE) to fulfill its main mission of allowing industrial wireless devices to access the core network. This mission is even harder when the GFRA requests are correlated because of event-driven activity triggers. In this work, we propose a new tractable gaussian correlated activity (GCA) model for this scenario. The corresponding AUDaCE problem is then studied in the Bayesian compressed sensing (BCS) framework. An hybrid instance of the generalized AMP (GAMP) algorithm is derived and its capability to perform AUDaCE is numerically assessed by extensive Monte-Carlo simulations. The numerical results show gains of 2.5dB in channel estimation gain for twice less detection errors w.r.t. state-of-theart algorithms.

But in grant-free random access, another key question is how devices should utilize resources without coordination. One standard solution to this problem are strategies where devices randomly select time-slots based on an optimized stochastic allocation rule. However, the optimization of this allocation rule requires accurate knowledge of which devices have been active in previous frames. As user identification algorithms are subject to errors, the expected throughput of the optimized allocation can be highly suboptimal. In 40, 48, 51, we propose algorithms for optimization of device time-slot allocations that mitigate the impact of user identification errors. We show that when the activity distribution with and without errors is known, then our algorithm converges with probability one to a stationary point. When the activity distributions are not available, we introduce new theoretically-motivated heuristics which significantly improve the expected throughput over existing algorithms and approach the performance when errors are not present.

8.2 Heterogeneous time constraints : fundamental limits

Participants: Malcolm Egan, Jean-Marie Gorce, Homa Nikbakht.

This line of research is lead by Jean-Marie Gorce and contributes to axis 1 and axis 2. The objective is to extend information theory to explore the fundamental limits of communications under heterogeneous time constraints.

A standard assumption in the design of ultra-reliable low-latency communication systems is that the duration between message arrivals is larger than the number of channel uses (in the information theory wording, a c.u. corresponds to one transmission symbol), before the decoding deadline. Nevertheless, this assumption fails when messages arrive rapidly and reliability constraints require that the number of channel uses exceed the time between arrivals. In 52, we consider a broadcast setting in which a transmitter wishes to send two different messages to two receivers over Gaussian channels. Messages have different arrival times and decoding deadlines such that their transmission windows overlap. For this setting, we propose a coding scheme that exploits Marton's coding strategy. We derive rigorous bounds on the achievable rate regions. Those bounds can be easily employed in point-to-point settings with one or multiple parallel channels. In the point-to-point setting with one or multiple parallel channels, the proposed achievability scheme outperforms the Normal Approximation, especially when the number of channel uses is smaller than 200. In the broadcast setting, our scheme agrees with Marton's strategy for sufficiently large numbers of channel uses and shows significant performance improvements over standard approaches based on time sharing for transmission of short packets.

In 53 we explore how random user activities and heterogeneous delay traffic can be taken into account to design efficient coding schemes and information-theoretic converse results. The heterogeneous traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent decoding delays. The total number of cooperation rounds at transmitter and receiver sides is limited to $D$ rounds. Each transmitter is active with probability $\rho \in [0,1]$. We consider two different models for the arrival of the mixed-delay traffic. We derive inner and outer bounds on the fundamental per-user multiplexing gain (MG) region of the symmetric Wyner network as well as inner bounds on the fundamental MG region of the hexagonal model. Our inner and outer bounds are generally very close and coincide in special cases.

8.3 Goal oriented communications

Participants: Malcolm Egan, Jean-Marie Gorce, Naveed Ahmad.

This line of research is lead by Malcolm Egan and covers axis 1 and axis 2. This research is in line with join communication and computing, in the context of goaol oriented communications. Since a receiver aims at computing a function, the objective is to learn simultaneously the encoding procedures and the classification function.

With the proliferation of cheap sensors and the ubiquity of cloud and edge computing, predictive/condition-based maintenance is expected to play an important role in smart homes and buildings. Nevertheless, a key difficulty is ensuring that sensors provide data of sufficient quality in order to reliably detect building (e.g., heating system) degradation in systems or comfort. At the same time, sensor utilization should be limited as much as possible in order to minimize power consumption and increase the lifetimes of batteries. A solution to this problem requires careful codesign of sensor communication and data analytics. In 27, 37, we introduce a formulation of this codesign problem, which is based on an optimization problem to jointly design how often data is collected and compression levels in order to balance the quality of fault detection with the quantity of transmitted data. To solve the optimization problem, we apply a differentiable search algorithm based on a variant of stochastic gradient descent for discrete optimization problems. We apply our codesign framework and solve the resulting optimization problem using data obtained from a building comfort experiment known as the Twin House Experiment. We also provide an extension of our algorithm to a dynamic variant of the codesign framework, where comfort levels and power consumption penalties are time varying. Numerical results show that our algorithm rapidly finds an efficient tradeoff between classifier accuracy and sensor power consumption.

8.4 Random tensors : from theory to applications in communications

Participants: Maxime Guillaud.

This line of research is lead by Maxime Guillaud and covers axis 1 and axis 2. Although the papers described below are more theoretic and cover general data analysis problems, their application is important in various communication problems such as massive access or channel charting.

In 33, asymmetric order$-d$ spiked tensor models with Gaussian noise are studied, relying on random matrix theory (RMT). Using the variational definition of the singular vectors and values of (Lim, 2005), we show that the analysis of the considered model boils down to the analysis of an equivalent spiked symmetric block-wise random matrix. Our approach allows the exact characterization of the almost sure asymptotic singular value and alignments of the corresponding singular vectors with the true spike components. In contrast to other works that rely mostly on tools from statistical physics to study random tensors, our results rely solely on classical RMT tools such as Stein's lemma. Finally, classical RMT results concerning spiked random matrices are recovered as a particular case. Then, 45, 35 study the deflation algorithm when applied to estimate a low-rank symmetric spike contained in a large tensor corrupted by additive Gaussian noise. Specifically, we provide a precise characterization of the large-dimensional performance of deflation in terms of the alignments of the vectors obtained by successive rank-1 approximation and of their estimated weights, assuming non-trivial (fixed) correlations among spike components. Our analysis allows an understanding of the deflation mechanism in the presence of noise and can be exploited for designing more efficient signal estimation methods. 46 provides a theoretical analysis of learning from data that have an underlying low-rank tensor structure in both supervised and unsupervised settings, under a simplified data model. For the supervised setting, we provide an analysis of a Ridge classifier (with high regularization parameter) with and without knowledge of the low-rank structure of the data. Our results quantify analytically the gain in misclassification errors achieved by exploiting the low-rank structure for denoising purposes, as opposed to treating data as mere vectors. We further provide a similar analysis in the context of clustering, thereby quantifying the exact performance gap between tensor methods and standard approaches which treat data as simple vectors.

8.5 Neural networks, communication and energy efficiency

Participants: Malcolm Egan, Claire Goursaud, Guillaume Marthe.

All Maracas permanent members have some works related to the use of Deep Neural Networks (DNN). Indeed, using DNN for some difficult tasks to be done in communications is quite standard and is one of the keystones elements for 6G. We may use machine learning for channel charting, for terminal identification, for sensing, for coding, etc... In this section we present two line of research devoted to the design of NN based solutions and focusing and energy efficiency. The first part study the DNN architecture itself and the second proposed the use of spike neurons for wake-up radio.

8.6 Quantum networks and communications

Participants: Claire Goursaud, Fabrice Dupuy, Muhammad Idham Habibie, Romain Piron.

This line of research is lead by Claire Goursaud and covers axis 2 and axis 4. The objective is to bridge the gap between network protocol design and quantum algorithms, assuming that in the future, some equipment could host a quantum computer. We explore how such equipement could improve the complexity of some protocols.

In 28, the key challenges (loss due to distance, entanglement routing, multi‐commodities) for the coming quantum internet, relying on entanglement of quantum bits (for short, qubits) on top of an existing network, are analyzed. A unifying framework enabling to compare the various entanglement distribution, purification, and routing protocols published so far is presented. With regard to entanglement routing, the introduction of different time windows will be essential in order to cope efficiently with the main challenges like complex route calculation and fidelity estimation on the one hand, actual entanglement route selection and entangled photon generation on the other hand. For a roll‐out on top of existing transmission networks, all the research publications for the last 20 years start to cover pretty well the global scheme. Nevertheless, open questions remain, like the actual advantage of some task execution prior to the online quantum path selection, or the design of algorithms approximating the multi‐commodities flow optimization problem, or the issue of dealing with a processing time not much longer than the qubit life time.

8.7 On channel charting

Participants: Maxime Guillaud, Yamil Vindas.

This research is lead by Maxime Guillaud and covers axes 1, 2 and 3. As detailed in 30, channel charting is a recently proposed framework that applies dimensionality reduction to channel state information (CSI) in wireless systems with the goal of associating a pseudo-position to each mobile user in a low-dimensional space: the channel chart. Channel charting summarizes the entire CSI dataset in a self-supervised manner, which opens up a range of applications that are tied to user location. In this article, we introduce the theoretical underpinnings of channel charting and present an overview of recent algorithmic developments and experimental results obtained in the field. We furthermore discuss concrete application examples of channel charting to network- and user-related applications, and we provide a perspective on future developments and challenges as well as the role of channel charting in next-generation wireless networks.

CC can be applied at the infrastructure basestation side with the goal of extracting pseudo-position information for each user. In 36, we focus on the practically relevant streaming CSI data scenario, in which CSI is constantly estimated. To deal with storage limitations, we develop a novel streaming CC architecture that maintains a small core CSI dataset from which the channel charts are learned. Curation of the core CSI dataset is achieved using a min-maxsimilarity criterion. Numerical validation with measured CSI data demonstrates that our method approaches the accuracy obtained from the complete CSI dataset while using only a fraction of CSI storage and avoiding catastrophic forgetting of old CSI data.

We also applied CC for Ultra-Reliable Low-Latency Communications (URLLC) since it relies on accurate knowledge of channel statistics. Exploiting the spatial consistency of channel statistics arises as a promising solution, allowing a base station to predict the propagation conditions and select the communication parameters for a new user from samples collected from previous users of the network. Based on this idea, 31 provides a timely framework to exploit long-range channel spatial correlation through so-called statistical radio maps, enabling URLLC communications with given statistical guarantees. The framework is exemplified by predicting the channel capacity distribution both in a location-based radio map and in a latent space rendered by a channel chart, the latter being a localization-free approach based on channel state information. It is also shown how to use the maps to select the transmission rate in a new location that achieves a target level of reliability.

8.8 Molecular Communication

Participants: Malcolm Egan.

This research is lead by Malcolm Egan . It cover axes 1 and 4 and the objective is to elaborate an information and communication theory appropriate for molecular communications. A more detailed definition is provided in 29: within many chemical and biological systems, both synthetic and natural, communication via chemical messengers is widely viewed as a key feature. Often known as molecular communication, such communication has been a concern in the fields of synthetic biologists, nanotechnologists, communications engineers, and philosophers of science. However, interactions between these fields are currently limited. Nevertheless, the fact that the same basic phenomenon is studied by all of these fields raises the question of whether there are unexploited interdisciplinary synergies. In this paper, we summarize the perspectives of each field on molecular communications, highlight potential synergies, discuss ongoing challenges to exploit these synergies, and present future perspectives for interdisciplinary efforts in this area.

The Transfer of ‘information’ via molecules is a theme that resonates across the realm of nature, underlying collective behavior, homeostasis, and many disorders and diseases, and potentially holding the answers to some of the life’s most profound questions. The prospects of understanding and manipulating this natural modality of communication have attracted a significant research interest from information and communication theorists (ICT) over the past two decades. The aim is to provide novel means of understanding and engineering biological systems. These efforts have produced substantial body of literature that sets the groundwork for bio-inspired, artificial Molecular Communication (MC) systems. This ICT-based perspective has also contributed to the understanding of natural MC, with many of the results from these endeavors being published in tha special issue co-edited by Malcolm Egan32.

To model biochemical reaction systems with diffusion one can either use stochastic, microscopic reaction-diffusion master equations or deterministic, macroscopic reaction-diffusion system. The connection between these two models is not only theoretically important but also plays an essential role in applications. In 50, we consider the macroscopic limits of the chemical reaction-diffusion master equation for first-order chemical reaction systems in highly heterogeneous environments. More precisely, the diffusion coefficients as well as the reaction rates are spatially inhomogeneous and the reaction rates may also be discontinuous. By carefully discretizing these heterogeneities within a reaction-diffusion master equation model, we show that in the limit we recover the macroscopic reaction-diffusion system with inhomogeneous diffusion and reaction rates.

In order to fully exploit the potential of molecular communication (MC) for intra-body communication, practically implementable cellular receivers are an important long-term goal. A variety of receiver architectures based on chemical reaction networks (CRNs) and gene-regulatory networks (GRNs) has been introduced in the literature, because cells use these concepts to perform computations in nature. However, practical feasibility is still limited by stochastic fluctuations of chemical reactions and long computation times in GRNs. Therefore, in 39, we propose two receiver designs based on stochastic CRNs, i.e., CRNs that perform computations by exploiting the intrinsic fluctuations of chemical reactions with very low molecule counts. The first CRN builds on a recent result from chemistry that showed how Boltzmann machines (BMs), a commonly used machine learning model, can be implemented with CRNs. While this approach yields a fixed CRN once deployed, our second approach based on a manually designed CRN can be trained with pilot symbols even within the cell and thus adapt to changing channel conditions. We extend the literature by showing that practical robust detectors can achieve close-to-MAP performance even without explicit channel knowledge.

9.1 Bilateral contracts with industry

Participants: Naveed Ahmad, Lelio Chetot, Malcolm Egan, Jean-Marie Gorce, Alix Jeannerot.

We have currently the following partnerships

1. Inria-Nokia Bell Labs common lab : Jean-Marie Gorce lead the future challenge LEarnNet starting on January, 1st, 2024. Alix Jeannerot is also partly funded in the framework of this common lab.
2. The SPIE-ICS / Insa Lyon chaire on IoT has been setup in 2017 by Jean-Marie Gorce for the benefit of the CITIlab. He was the head of this chair from 2016 to 2019, transfered to Frédéric Le Mouel in sept 2019. The remaining budget for Maracas corresponds to one postdoc ended in 2022 (Naveed Ahmad). The work has been published in 2023 37, 27.

9.2 Bilateral grants with industry

Participants: Jean-Marie Gorce, Shashwat Mitra, Fabrice Dupuy, Shashwat Mishra, Claire Goursaud, Guillaume Villemaud, Shanglin Yang.

1. CIFRE with Nokia Bell Labs (2021-2024) on IA For resource allocation in IoT networks. This work is adressed by Mr Shashwat Mishra (PhD student), to be defended in 2024 44.
2. CIFRE with Orange Labs (2022-2025) on passive TAG aided localization with zero-energy-devices anchors. This work is adressed by Mr Shanglin Yang (PhD student), to be defended in 2025.
3. CIFRE Orange Labs (2022-2025) on quantum algorithms for networks. This project is developed in the PhD of Fabrice Dupuy 28.

10.1 International initiatives

10.2 International research visitors

10.3 European initiatives

10.4 National initiatives

11.1 Promoting scientific activities