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Intelligent Decision-Making for Reliable Information Transfer in Next-Generation Wireless Networks
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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
- Degree
- Doctor
- Publisher
- DGIST
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