Leonardo S Cardoso | Searcharxiv

arXiv2026

An Experimental Assessment of the Spatial and Frequency Selectivity of Reconfigurable Intelligent Surfaces

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.

cs.NI↗

arXiv2020

A Technical Review of Wireless security for the Internet of things: Software Defined Radio perspective

The increase of cyberattacks using IoT devices has exposed the vulnerabilities in the infrastructures that make up the IoT and have shown how small devices can affect networks and services functioning. This paper presents a review of the vulnerabilities of the wireless technologies that bear the IoT and assessing the experiences in implementing wireless attacks targeting the Internet of Things using Software-Defined Radio (SDR) technologies. A systematic literature review was conducted. The types of vulnerabilities and attacks that can affect the wireless technologies that stand the IoT ecosystem and SDR radio platforms were compared. On the IoT system model layer, perception layer was identified as the most vulnerable. Most attacks at this level occur due to limitations in hardware, physical exposure of devices, and heterogeneity of technologies. Future cybersecurity systems based on SDR radios have notable advantages due to their flexibility to adapt to new communication technologies and their potential for the development of advanced tools. However, cybersecurity challenges for the Internet of Things are so complex that it is needed to merge SDR hardware with cognitive techniques and intelligent techniques such as deep learning to adapt to rapid technological changes.

cs.CR↗