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Department of Electrical Engineering and Computer Science
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Ph.D.
Intelligent Decision-Making for Reliable Information Transfer in Next-Generation Wireless Networks
Sinwoong Yun
Department of Electrical Engineering and Computer Science
Theses
Ph.D.
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Title
Intelligent Decision-Making for Reliable Information Transfer in Next-Generation Wireless Networks
DGIST Authors
Sinwoong Yun
;
Sungjin Lee
;
Jemin Lee
Advisor
이성진
Co-Advisor(s)
Jemin Lee
Issued Date
2025
Awarded Date
2025-02-01
Citation
Sinwoong Yun. (2025). Intelligent Decision-Making for Reliable Information Transfer in Next-Generation Wireless Networks. doi: 10.22677/THESIS.200000840042
Type
Thesis
Description
IoT networks, data freshness, reinforcement learning, intelligent prediction, physical layer authentication
Table Of Contents
I. Learning-based Sensing and Computing Decision for Data Freshness in Edge Computing-enabled Networks 1
1.1 Introduction 1
1.2 Edge Computing-enabled Wireless Sensor Networks Model 4
1.2.1 Network Description 4
1.2.2 Transmission Model 5
1.2.3 Energy Model 6
1.2.4 η-Coverage Probability 7
1.2.5 Two SCD Approaches 10
1.3 Probability-based SCD Algorithm 10
1.3.1 Single Pre-Charged Sensor Case 11
1.3.2 Multiple EH Sensor Case 20
1.4 RL-based SCD Algorithm 20
1.4.1 POMDP Formulation 21
1.4.2 Proposed Network Architecture 22
1.4.3 Complexity Analysis 24
1.5 Simulation Results 25
1.6 Conclusion 30
II. i-CU: Intelligent Cache Replacement and Content Update for Data Freshness in Cloud-Edge Networks 32
2.1 Introduction 32
2.2 System Model and Problem Formulation 35
2.2.1 Network Description and Age of Information 35
2.2.2 Content Request and Transmission Model 38
2.2.3 FACH Ratio Maximization Problem 39
2.3 Intelligent Cache Replacement and Content Update Algorithm 41
2.3.1 MDP Formulation 41
2.3.2 Score-based Action Decision 42
2.3.3 Proposed Algorithm 44
2.3.4 Complexity Analysis 47
2.4 Simulation Results 49
2.4.1 Simulation Setting and Baseline Algorithms 49
2.4.2 Training Curve and Performance Verification 51
2.4.3 Effect of Environmental Parameters on Performance 53
2.5 Conclusion 56
III.Channel Feature Prediction-based Physical Layer Authentication in Underwater Networks 58
3.1 Introduction 58
3.2 CIR Feature Prediction-based Authentication 59
3.2.1 Network Model 59
3.2.2 Feature Prediction-based Authentication 60
3.3 Simulation Results 61
3.4 Conclusion 62
References 63
URI
http://hdl.handle.net/20.500.11750/57997
http://dgist.dcollection.net/common/orgView/200000840042
DOI
10.22677/THESIS.200000840042
Degree
Doctor
Department
Department of Electrical Engineering and Computer Science
Publisher
DGIST
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