Practical Implementation of RIS-Aided Spectrum Sensing: A Deep Learning-Based Solution
For cognitive radio systems, this work demonstrates that RIS can enhance deep learning-based spectrum sensing, potentially improving spectrum utilization in next-generation wireless communications.
The paper proposes a deep learning-based spectrum sensing method aided by reconfigurable intelligent surfaces (RIS) for next-generation cognitive radios, using object detection approaches (Detectron2 and YOLOv7) on spectrograms of 4G LTE and 5G NR signals. Experiments with a real RIS prototype show consistent and significant improvement in detecting primary transmitter signal type, time, and frequency utilization.
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.