Hybrid Machine-Learning-Based Spectrum Sensing and Allocation With Adaptive Congestion-Aware Modeling in CR-Assisted IoV Networks-Reference-Cited by-同舟云学术

Hybrid Machine-Learning-Based Spectrum Sensing and Allocation With Adaptive Congestion-Aware Modeling in CR-Assisted IoV Networks

Published:2022-12-15 Issue:24 Volume:9 Page:25100-25116
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Ahmed Ramsha¹^ORCID,Chen Yueyun²^ORCID,Hassan Bilal³^ORCID,Du Liping²,Hassan Taimur⁴^ORCID,Dias Jorge³^ORCID

Affiliation:

1. Department of Biomedical Engineering, Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, UAE

2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

3. Khalifa University Center for Autonomous Robotic Systems, Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, UAE

4. Department of Electrical Engineering and Computer Science, Center for Cyber-Physical Systems, Khalifa University of Science and Technology, Abu Dhabi, UAE

Funder

Industry Oriented 5G Network Standard Verification Public Service Platform

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/9973413/09845681.pdf?arnumber=9845681

Reference60 articles.

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2. Resource Allocation Based on Deep Neural Networks for Cognitive Radio Networks

3. Activity Pattern Aware Spectrum Sensing: A CNN-Based Deep Learning Approach