1 research outputs found
Practical Implementation of RIS-Aided Spectrum Sensing: A Deep Learning-Based Solution
This paper presents reconfigurable intelligent surface (RIS)-aided deep
learning (DL)-based spectrum sensing for next-generation cognitive radios. To
that end, the secondary user (SU) monitors the primary transmitter (PT) signal,
where the RIS plays a pivotal role in increasing the strength of the PT signal
at the SU. The spectrograms of the synthesized dataset, including the 4G LTE
and 5G NR signals, are mapped to images utilized for training the state-of-art
object detection approaches, namely Detectron2 and YOLOv7. By conducting
extensive experiments using a real RIS prototype, we demonstrate that the RIS
can consistently and significantly improve the performance of the DL detectors
to identify the PT signal type along with its time and frequency utilization.
This study also paves the way for optimizing spectrum utilization through
RIS-assisted CR application in next-generation wireless communication systems.Comment: Accepted in IEEE Systems Journal, Copyright IEE