无人机无线通信中的资源与干扰管理

发布时间：2021-04-25 14:26

　　无人机（UAVs）己成为无线网络的重要组成部分,同时也是5G和未来无线物联网的关键推动因素。UAV作为空中基站,在覆盖范围、连接性和频谱方面提高蜂窝网络性能。无人机机载基站可提供高质量的网络连接并扩展无线蜂窝网络的覆盖范围。此外,UAV可以作为蜂窝网络内的飞行移动终端,支持实时视频流以及物品的递送等多种应用。然而,在UAVs无线通信网络体系的设计和部署中仍然存在许多具有挑战性的问题,如能量和干扰管理。博弈论利用无线网络中节点的理性行为、动态的环境以及节点的偏好,是分析和建模无人机辅助网络的有效工具。因此,本文试图利用博弈论技术解决无人机无线通信网络的资源和干扰管理问题。首先,使用纳什议价博弈研究并优化两架合作无人机作为空中基站的能效。其次,采用先进的平均场博弈（MFG）研究了密集无人机机载基站中的干扰和能量约束问题。第三,利用MFG解决了大规模蜂窝连接无人机对地面基站造成干扰的问题。具体来说,本文的主要贡献如下:1.为确保UAVs机载基站的高可用性,性能可接受性和经济可行性,能量消耗的优化是重中之重。提出纳什议价博弈论方法,用于无人机辅助网络中的能效优化。针对无人机基站的自适应信标周期... 

【文章来源】：西安电子科技大学陕西省 211工程院校 教育部直属院校

【文章页数】：116 页

【学位级别】：博士

【文章目录】：
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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