Publication: Practical implementation of RIS-Aided spectrum sensing: a deep-learning-based solution
Program
School / College / Institute
College of Engineering
Laboratory
Laboratory
KU Authors
Co-Authors
Hokelek, I (Hokelek, Ibrahim)
Gorcin, A (Gorcin, Ali)
Publication Date
Language
Type
Embargo Status
Journal Title
Journal ISSN
Volume Title
Alternative Title
Abstract
This article presents reconfigurable intelligent surface (RIS)-aided deep learning (DL)-based spectrum sensing for next-generation cognitive radios (CRs). 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 fourth-generation long-term evolution and fifth-generation new radio signals, are mapped to images utilized for training the state-of-the-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.
Source
Publisher
IEEE
Subject
Computer Science, Telecommunications
Citation
Has Part
Source
IEEE Systems Journal
Book Series Title
Edition
DOI
10.1109/JSYST.2024.3376986