無(wú)人機(jī)（UAVs）己成為無(wú)線網(wǎng)絡(luò)的重要組成部分,同時(shí)也是5G和未來(lái)無(wú)線物聯(lián)網(wǎng)的關(guān)鍵推動(dòng)因素。UAV作為空中基站,在覆蓋范圍、連接性和頻譜方面提高蜂窩網(wǎng)絡(luò)性能。無(wú)人機(jī)機(jī)載基站可提供高質(zhì)量的網(wǎng)絡(luò)連接并擴(kuò)展無(wú)線蜂窩網(wǎng)絡(luò)的覆蓋范圍。此外,UAV可以作為蜂窩網(wǎng)絡(luò)內(nèi)的飛行移動(dòng)終端,支持實(shí)時(shí)視頻流以及物品的遞送等多種應(yīng)用。然而,在UAVs無(wú)線通信網(wǎng)絡(luò)體系的設(shè)計(jì)和部署中仍然存在許多具有挑戰(zhàn)性的問(wèn)題,如能量和干擾管理。博弈論利用無(wú)線網(wǎng)絡(luò)中節(jié)點(diǎn)的理性行為、動(dòng)態(tài)的環(huán)境以及節(jié)點(diǎn)的偏好,是分析和建模無(wú)人機(jī)輔助網(wǎng)絡(luò)的有效工具。因此,本文試圖利用博弈論技術(shù)解決無(wú)人機(jī)無(wú)線通信網(wǎng)絡(luò)的資源和干擾管理問(wèn)題。首先,使用納什議價(jià)博弈研究并優(yōu)化兩架合作無(wú)人機(jī)作為空中基站的能效。其次,采用先進(jìn)的平均場(chǎng)博弈（MFG）研究了密集無(wú)人機(jī)機(jī)載基站中的干擾和能量約束問(wèn)題。第三,利用MFG解決了大規(guī)模蜂窩連接無(wú)人機(jī)對(duì)地面基站造成干擾的問(wèn)題。具體來(lái)說(shuō),本文的主要貢獻(xiàn)如下:1.為確保UAVs機(jī)載基站的高可用性,性能可接受性和經(jīng)濟(jì)可行性,能量消耗的優(yōu)化是重中之重。提出納什議價(jià)博弈論方法,用于無(wú)人機(jī)輔助網(wǎng)絡(luò)中的能效優(yōu)化。針對(duì)無(wú)人機(jī)基站的自適應(yīng)信標(biāo)周期... 

【文章來(lái)源】：西安電子科技大學(xué)陜西省 211工程院校 教育部直屬院校

【文章頁(yè)數(shù)】：116 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
Abstract
摘要
Abbreviations
Chapter 1 Introduction
    1.1 Background of Wireless Communication with Unmanned Aerial Vehicles
        1.1.1 Unmanned Aerial Base Stations
        1.1.2 Advantages of Unmanned Aerial Small Cell Networks over Terres-trial Base Stations
        1.1.3 Mobile Networks Connected UAVs
    1.2 Challenges Facing Wireless Communications with UAVs
    1.3 Proposed Solution
    1.4 Main Contributions and Thesis Organization
Chapter 2 Preliminaries, Background, and Literature Review
    2.1 Game Theory
        2.1.1 Fundamentals of Game Theory
        2.1.2 Game Theory in UAV-assisted Networks
    2.2 Bargaining Games
        2.2.1 The Nash Bargaining Solution
    2.3 Mean Field Game
        2.3.1 Background of Mean field game
        2.3.2 Basics of Mean Field Games
        2.3.3 Mean Field
        2.3.4 HJB and FPK equations
        2.3.5 Shortcomings and Limitations of MFGs
    2.4 Literature Review
    2.5 General Resource and Interference Management Schemes
    2.6 Game Theoretic Approaches for Resource and Interference Management
        2.6.1 Traditional Game Theoretic Approaches for Resource and Interfer-ence Management
        2.6.2 MFG for Resource and Interference Management
    2.7 Chapter Summary
Chapter 3 Energy Efficiency Optimization for Wireless Sparse Unmanned Aerial Ve-hicles Communication Networks: A Bargaining Game Approach
    3.1 System Description
    3.2 Game Formulation Cooperative Strategy based on NBS
        3.2.1 Utility Function
        3.2.2 Bargaining Games
        3.2.3 The Nash Bargaining Solution
    3.3 Simulation Results and Discussions
    3.4 Chapter Summary
Chapter 4 Interference and Resource Management for Cellular-enabled UnmannedAerial Vehicles: A Mean Field Game Approach
    4.1 System Model Description
        4.1.1 Network Propagation Model
        4.1.2 Interference Interaction Model
        4.1.3 Interference Mean Field
    4.2 Differential Game Formulation
        4.2.1 State Space Dynamics
        4.2.2 Cost Function of a UAV
        4.2.3 Optimal Control Problem
    4.3 Interference Mitigation Mean Field Game
        4.3.1 Mean Field and Mean Field Approximation
        4.3.2 Backward equation
        4.3.3 Forward equation
    4.4 Distributed Policy Based on the Finite Difference Method
        4.4.1 Solution to the Forward Equation
        4.4.2 Solution to the Backward Equation
        4.4.3 Distributed Control Policy
    4.5 Results and Discussion
        4.5.1 Characteristics of Mean Field at Mean Field Equilibrium
        4.5.2 Performance Metrics
        4.5.3 Performance Results
    4.6 Conclusion
Chapter 5 Interference and Energy Management for Dense Aerial Access Networks:A Mean Field Game Approach
    5.1 System Model Description
    5.2 Differential Game Formulation
        5.2.1 State Space
        5.2.2 Cost Function for a Generic AAN
        5.2.3 Optimal Control Problem
    5.3 Mean Field Game for Power and Velocity Control
        5.3.1 Mean Field and Mean Field Approximation
        5.3.2 Hamilton-Jacobi-Bellman Equation
        5.3.3 Fokker-Planck-Kolmogorov Equation
    5.4 Mean Field Game Solution
        5.4.1 Mean Field Equilibrium
        5.4.2 Solution to the FPK equation
        5.4.3 Solution to the HJB equation
        5.4.4 Distributed Control Policy
    5.5 Numerical Results and Discussion
        5.5.1 Simulation Settings
        5.5.2 Characteristics of Mean Field at Equilibrium
        5.5.3 Performance Metrics
        5.5.4 Performance Results
    5.6 Chapter Summary
Chapter 6 Conclusions and Future Works
    6.1 Conclusions
    6.2 Future Research Directions
        6.2.1 Optimal Placement and Trajectory for Massive UAV-based Network
        6.2.2 Advanced Distributed Schemes for Interference Management
        6.2.3 Feasibility of UAVs in 5G networks
References
Acknowledgements
Publications



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