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.