Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach

Published:2024-02-01 Issue:3 Volume:11 Page:4899-4913
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Lin Ruiquan¹^ORCID,Qiu Hangding¹^ORCID,Wang Jun¹^ORCID,Zhang Zaichen²^ORCID,Wu Liang²^ORCID,Shu Feng³^ORCID

Affiliation:

1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China

2. National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, China

3. School of Information and Communication Engineering, Hainan University, Haikou, China

Funder

Natural Science Foundation of China

Industry–Academia Collaboration Program of Fujian Universities

Hainan Province Science and Technology Special Fund

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/ielx7/6488907/10413935/10198554.pdf?arnumber=10198554

Reference41 articles.

1. Cell-Free Satellite-UAV Networks for 6G Wide-Area Internet of Things

2. Performance Analysis of Clustering Car-Following V2X System with Wireless Power Transfer and Massive Connections

3. A Reliable Energy Efficient Dynamic Spectrum Sensing for Cognitive Radio IoT Networks

4. Energy-Efficient Resource Allocation for Cognitive Industrial Internet of Things With Wireless Energy Harvesting

5. Performance Analysis of Overlay Cognitive NOMA Systems With Imperfect Successive Interference Cancellation

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