RL-IoT: Reinforcement Learning-Based Routing Approach for Cognitive Radio-Enabled IoT Communications-Reference-Cited by-同舟云学术

RL-IoT: Reinforcement Learning-Based Routing Approach for Cognitive Radio-Enabled IoT Communications

Published:2023-01-15 Issue:2 Volume:10 Page:1836-1847
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Safdar Malik Tauqeer¹^ORCID,Razzaq Malik Kaleem¹^ORCID,Afzal Ayesha¹^ORCID,Ibrar Muhammad²^ORCID,Wang Lei²^ORCID,Song Houbing³^ORCID,Shah Nadir⁴^ORCID

Affiliation:

1. Department of Computer Science, Air University, Multan, Pakistan

2. School of Software, Dalian University of Science and Technology, Dalian, China

3. Department of Information Systems, Baltimore County, University of Maryland, Baltimore, MD, USA

4. Department of Computer Science, COMSATS University Islamabad (Wah Campus), Rawalpindi, Pakistan

Funder

National Nature Science Foundation of China

Science and Technology Major Industrial Project of Liaoning Province

Dalian Science and Technology Innovation Fund

Fundamental Research Funds for the Central Universities

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/ielx7/6488907/10007705/09905718.pdf?arnumber=9905718

Reference40 articles.

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2. A Survey on Routing Protocols for Delay and Energy-Constrained Cognitive Radio Networks

3. On the convergence of no-regret learning in selfish routing;krichene;Proc Int Conf Mach Learn,2014

4. Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

5. Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games

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