Cyrille Morin

Cyrille Morin, Leonardo S Cardoso, Maxime Guillaud et al.

This work investigates the impact of reconfigurable intelligent surfaces (RIS) on radio links other than the one for which the RIS configuration is optimized. We consider three different scenarios in which a secondary communication link could be affected by a RIS whose configuration is optimized for a primary communication link operating in the vicinity, on the same or on different frequencies. This question is investigated experimentally in the FR1 band, using the CorteXlab radio testbed and a Greenerwave RIS. We show that the impact, in terms of received power and impact on the channel phase of the secondary link, is significant even outside of the nominal frequency range of the RIS, and is not mitigated by carrier frequency separation between the two communication links.

Cyrille Morin, Leonardo Cardoso, Jakob Hoydis et al.

Hardware imperfections in RF transmitters introduce features that can be used to identify a specific transmitter amongst others. Supervised deep learning has shown good performance in this task but using datasets not applicable to real world situations where topologies evolve over time. To remedy this, the work rests on a series of datasets gathered in the Future Internet of Things / Cognitive Radio Testbed [4] (FIT/CorteXlab) to train a convolutional neural network (CNN), where focus has been given to reduce channel bias that has plagued previous works and constrained them to a constant environment or to simulations. The most challenging scenarios provide the trained neural network with resilience and show insight on the best signal type to use for identification , namely packet preamble. The generated datasets are published on the Machine Learning For Communications Emerging Technologies Initiatives web site 4 in the hope that they serve as stepping stones for future progress in the area. The community is also invited to reproduce the studied scenarios and results by generating new datasets in FIT/CorteXlab.