Practical Implementation of RIS-Aided Spectrum Sensing: A Deep Learning-Based Solution

TL;DR

This paper demonstrates that reconfigurable intelligent surfaces significantly enhance deep learning-based spectrum sensing in cognitive radios, improving detection accuracy for 4G LTE and 5G signals through real-world experiments.

Contribution

It introduces a practical RIS-assisted spectrum sensing framework using deep learning and real RIS hardware, advancing spectrum detection capabilities in next-generation wireless systems.

Findings

RIS improves detection accuracy of PT signals

Deep learning models effectively classify 4G LTE and 5G signals

Real RIS prototype validates performance improvements

Abstract

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.