ComIT research team

Research activities

• Waveform design: efficiency, robustness, and security

We propose “information shaping” functions suited to realistic propagation channels. We target spectral or energy efficiency while taking into account robustness and security constraints. We develop wiretap channel codes, faster-than-Nyquist transmission, and multifunctional waveforms (e.g., radar and communications). We also consider machine learning to design optimal waveforms and their decoders (e.g., auto-encoders).

• Advanced receiver techniques

We design advanced receivers to face challenging observation models. We rely on Bayesian or supervised learning estimation/detection techniques (e.g., Monte-Carlo Markov chain, neural networks). We are also interested in reduced-complexity schemes such as turbo-equalization or variational Bayesian filtering for phase tracking.

• Efficient resource allocation in a multiuser environment

We study dynamic spectral allocation/usage techniques in multiple user scenarios (cooperative or non-cooperative). A first theoretical approach includes deriving fundamental limits (e.g., capacity regions). Secondly, we design channel random access methods and network coding tailored to aeronautical and space systems.

Examples of on-going research topics

Spectrum sharing between radar and communications

A single waveform can fulfill simultaneously both functions to save spectral resources and ease integration of underlying electronic building blocks (e.g., shared front-end and antennas).

Physical layer security

Randomness caused by the propagation channel and by radio-frequency components can be exploited to ensure secured communications (including key generation methods), thereby supplementing traditional cryptographic techniques.

Wideband radar

Wideband radars, usually used in radar imaging, can also be used for moving target detection. A nonambiguous mode can be obtained by simultaneously exploiting target range migration and sparsity of the scene.

Machine learning for physical layer

Neural networks can support end-to-end communication functions (or transmitter/receiver building blocks), at a very-low computational cost or for non-closed-form channel models.

Job offers

• PhD proposal - Distributed wideband beamforming for satellite communication swarms - Prof. Wallace Martins

Platforms

• RAdio Logicielle pour la Formation (RALF)
• Vehicular radar/communication transceiver station (RadCom)

9 December 2020 Research

More info and contacts

For more info and informal inquiries, please take a look at our professional webpages!