Annonce

With the popularisation of software defined radios (SDRs), an malevolent actor can deploy radio systems for interfering with legitimate communications, communicating on unlicensed bands and listening to private communications. In this proposed PhD work we target in a first moment spectrum sensing capabilities, that can be used to automatically locate and classify opponent transmissions, characterising it in terms of center frequency, occupied bandwidth, activity pattern, modulation and coding schemes, frame structure and more. Then we will study the identification problem, trying to uniquely single out individual transmitters among all transmitters. The proposed work will (i) create good datasets to train and test systems for spectrum sensing and (ii) develop deep learning (DL) systems for spectrum sensing/classification and identification.

Nowadays, spectrum surveillance is mainly done with relatively simple systems that require intense human intervention. However, as radio communications systems grow more and more complex in nature and can span larger portions of the spectrum, relying on human-based surveillance risks missing out on improper use of the spectrum. Sophisticated means to detect these transmissions, identify them and locate their source is thus necessary, but remains a complicated task to accomplish.