Mingun Kim. (2025). Modeling and Optimization of Network Resource Allocation for Edge Computing-Enabled Integrated Access and Backhaul Networks. doi: 10.22677/THESIS.200000840198
Type
Thesis
Description
Edge computing-enabled networks, Service caching, Multi-user multiple-input single-output, Integrated access and backhaul networks, Convex optimization, Reinforcement learning
Table Of Contents
I. INTRODUCTION 1 1.1 Outline and Contributions 2 1.1.1 Chapter 2 3 1.1.2 Chapter 3 3 1.1.3 Chapter 4 4 1.1.4 Chapter 5 4 II. Joint Service Caching and Computing Resource Allocation for Edge Computing-Enabled Networks 5 2.1 Introduction 5 2.2 System Model 8 2.2.1 Network Model 8 2.2.2 Service Caching and Computation Model 9 2.2.3 Communication Model 11 2.2.4 Performance Metric 12 2.3 Successful Service Probability Analysis 12 2.3.1 Successful Uplink and Downlink Transmission Probabilities 12 2.3.2 Successful Computation Probability 13 2.3.3 Successful Service Probability 15 2.4 Joint Optimization of Service Caching Distribution and Computing Resource Allocation 15 2.4.1 Problem Formulation 16 2.4.2 Optimal Solution 16 2.5 Asymptotic Solution for Joint Optimization of Service Caching Distribution and Computing Resource Allocation 21 2.5.1 Asymptotic Optimal Solution For Infinite Computing Capability Case 23 2.5.2 Near-Optimal Solution In High Computing Capability Region 24 2.6 Numerical Results 30 2.7 Conclusion 35 III.Joint Millimeter-Wave Beamforming Design and Access Link Rate Assignment for Integrated Access and Backhaul Networks 37 3.1 Introduction 37 3.2 System Model 39 3.2.1 Network Model 39 3.2.2 Communication Model 41 3.3 Expected Sum Data Rate Analysis 45 3.3.1 Approximated Coverage Probability 45 3.3.2 Achievable Sum Data Rate 46 3.4 joint beamforming design and access link rate assignment (JBA) Algorithm 48 3.4.1 Problem Formulation 49 3.4.2 Optimal Solution 50 3.4.3 Near-Optimal Solution 54 3.5 Numerical Results 55 3.6 Conclusion 61 IV. Intelligent power allocation in integrated access and backhaul networks with the buffering system 62 4.1 Introduction 62 4.2 System Model 63 4.2.1 Network Model 63 4.2.2 Communication Model 64 4.2.3 Achievable Sum Data Rate 66 4.3 reinforcement learning (RL)-based Beamforming Design Algorithm 66 4.3.1 Problem Formulation 67 4.3.2 Markov decision process (MDP) Formulation 67 4.3.3 proximal policy optimization (PPO) Algorithm 68 4.4 Simulation Results 69 4.5 Conclusion 71 V. CONCLUSIONS 72 References 74
