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.

翻译：本文提出了一种基于可重构智能表面（RIS）辅助的深度学习（DL）频谱感知方法，应用于下一代认知无线电系统。为此，次级用户（SU）监测主发射机（PT）信号，其中RIS在增强SU处PT信号强度方面发挥关键作用。将合成数据集的频谱图（包含4G LTE和5G NR信号）映射为图像，用于训练最先进的目标检测方法，即Detectron2和YOLOv7。通过使用真实RIS原型进行大量实验，我们证明RIS能够持续且显著提升DL检测器的性能，使其识别PT信号类型及其时频占用情况。本研究也为通过RIS辅助的认知无线电应用优化下一代无线通信系统中的频谱利用铺平了道路。