Team presentation

MARACAS combines communication theory and information theory with statistical signal processing, control theory,
and game theory to explore the field of Computing Networks. Wireless networks is the emblematic application for Maracas, but other scenarios are appealing for us, such as molecular communications, smart grids, or smart buildings.

Computing Networks generalize multi-user systems under the communication perspective, by 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, latency, 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.

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

  • to develop a sound mathematical  framework inspired by information theory.
  • to design algorithms, achieving performance close to these limits.
  • to test and validate these algorithms on experimental testbeds (e.g. FIT/CorteXlab)


MARACAS builds upon previous research activities developed under the banner of the SOCRATE team, where we focused on (a) the fundamental limits of multi-user communications; and (b) the design of algorithms for cognitive radio networks. In particular, MARACAS members are the main contributors to the development of FIT/CorteXlab, with contributions on software developments, signal processing implementation, evaluation and driving experiments in cooperation with many external partners. MARACAS leverages on this experience to address the area of autonomous distributed systems, from the angle of communication theory.

MARACAS develops sound mathematical models relying on information theory, with the challenge of addressing the complexity of computing networks due to the number of parameters and constraints to be considered. MARACAS also investigates on recent results from learning and artificial intelligence and their adaptation to multi-user communication systems and will contribute to bridge the gap between theory and experimentation.

Research themes

Axis 1 – Fundamental Limits of Reliable Communication Systems

Information theory is revisited to integrate reliability in the wide sense. 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 or for multi-constrained systems. This also calls for revisiting the fundamental estimation-detection problem in a general multi-criteria, multi-user framework to derive tractable and meaningful bounds.

The strategy of caching at the edge proposed for cellular networks shows the high potential of considering simultaneously data and network properties. In the coming years, Maracas will target to extend his skills on source coding aspects to tackle with a data-oriented modeling of Computing Networks.

Axis 2 – Algorithms and protocols

The 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 algorithms.

A less conventional approach relies on the exploitation of machine learning techniques, which can be seen as the natural evolution of cognitive radio based approaches. Machine learning is not new in radio networks, but was previously mostly based on reinforcement learning approaches. The apparition 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. Our primary objective is to identify strategic problems and then we will aim at contributing 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. The team activity leverages on the FIT/CorteXlab platform, and our strong partnerships with leading industry including Nokia Bell Labs, Orange labs, Sigfox or Sequans.
Beyond FIT/CorteXlab, we develop a strong expertise in experimental prototyping of radio systems in GNU Radio environment. Our experimental work also relies on collaborations with other Inria teams especially in the Rhone-Alpes centre but also within the future SILECS project which will implement the convergence between FIT and Grid’5000 infrastructures.
SILECS will represent an exciting and unique framework to test our algorithms, and to generate data, as required for machine learning based algorithms.

Last but not least, As software radio is becoming a technology accessible to anybody at a very low price, we may see in a near future more and more home-made and non official radio systems to be deployed for Internet of things and more generally for more and more applications. It is therefore essential to develop the skills to detect, analyse and control the spectrum usage. Our development on FIT/CorteXlab may contribute to this know-how.

Axis 4 – Other application fields

Beyond the study of wireless network, Maracas targets to broaden its exploratory playground from an application perspective. We are therefore 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 will 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 or multi-robots systems as studied by Chroma team).

We have some contributions about smart grids, molecular communications or smart-building networks.
The objective of this axis is to exploit our scientific background on new problems, in collaboration with other academic teams or industry.
This research axis may strengthen transfer and partnership, in collaboration with other Inria teams.


International and industrial relations

Main international relationships

Prof. Ignaki Estola (Sheffield University, UK)

Prof. Gareth Peters (Heriot-Watt, UK)

Prof. Trung Q. Duong (Queen’s Universirty, Belfast)

Prof. Visa Koivunen (Alto Univesity, Finland)

Prof. Petar Popovski (Aalborg University, Denmark)

Prof. H. Vincent Poor, (Princeton University, NJ, USA)

Ivan Sesklar, (Rutgers University, NJ, USA)


Main industrial relationships

Nokia Bell Labs (joint Inria-Nokia Bell Labs laboratory)

SPIE ICS (industrial chaire Insa-SPIE on Internet of Things)

Orange Labs

Sigfox, Sequans, R-tone

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