摘 要
随着现代通信技术的快速发展，频谱资源日益紧张，干扰问题成为制约通信系统性能提升的关键因素之一。为应对这一挑战，本文深入研究了通信系统中的干扰协调与抑制方法，旨在通过优化资源配置和信号处理策略，有效降低干扰对系统性能的影响。研究从理论建模、算法设计及实验验证三个层面展开，提出了一种基于机器学习的动态干扰协调机制，该机制能够根据实时信道状态信息自适应调整功率分配和频率规划，从而显著提升系统的频谱效率和用户公平性。同时，针对强干扰场景，本文引入了联合空域与时频域的混合抑制技术，进一步增强了系统的抗干扰能力。
关键词：干扰协调 机器学习 频谱效率
Abstract
With the rapid development of modern communication technology, spectrum resources are increasingly tight, and the interference problem has become one of the key factors restricting the improvement of communication system performance. To meet this challenge, this paper studies the methods of interference coordination and suppression in the communication system, aiming to effectively reduce the impact of interference on system performance by optimizing resource allocation and signal processing strategy. The research follows from theoretical modeling, algorithm design and experimental verification, and proposes a dynamic interference coordination mechanism based on machine learning, which can adaptively adjust power allocation and frequency planning according to real-time channel state information, so as to significantly improve the spectrum efficiency and user fairness of the system. At the same time, for the strong interference scenario, this paper introduces the hybrid suppression technology of the joint airspace and the time-frequency domain, which further enhances the anti-interference ability of the system.
Keyword:Interference Coordination Machine Learning Spectrum Efficiency
目 录
1绪论 1
1.1通信系统干扰问题的背景与意义 1
1.2干扰协调与抑制方法的研究现状 1
1.3本文研究方法与技术路线 2
2干扰协调的基本理论与方法 2
2.1干扰协调的概念与分类 2
2.2干扰协调的数学建模分析 3
2.3基于资源分配的干扰协调方法 3
2.4干扰协调在多用户场景中的应用 4
3干扰抑制的关键技术与实现 4
3.1干扰抑制的技术原理概述 4
3.2基于滤波器设计的干扰抑制方法 5
3.3自适应干扰抑制算法的研究 5
3.4实时干扰抑制系统的性能评估 6
4干扰协调与抑制的综合优化策略 6
4.1干扰协调与抑制的联合优化框架 6
4.2基于机器学习的干扰管理方法 7
4.3跨层优化在干扰管理中的应用 7
4.4干扰管理策略的实际案例分析 8
结论 8
参考文献 10
致谢 11
随着现代通信技术的快速发展，频谱资源日益紧张，干扰问题成为制约通信系统性能提升的关键因素之一。为应对这一挑战，本文深入研究了通信系统中的干扰协调与抑制方法，旨在通过优化资源配置和信号处理策略，有效降低干扰对系统性能的影响。研究从理论建模、算法设计及实验验证三个层面展开，提出了一种基于机器学习的动态干扰协调机制，该机制能够根据实时信道状态信息自适应调整功率分配和频率规划，从而显著提升系统的频谱效率和用户公平性。同时，针对强干扰场景，本文引入了联合空域与时频域的混合抑制技术，进一步增强了系统的抗干扰能力。
关键词：干扰协调 机器学习 频谱效率
Abstract
With the rapid development of modern communication technology, spectrum resources are increasingly tight, and the interference problem has become one of the key factors restricting the improvement of communication system performance. To meet this challenge, this paper studies the methods of interference coordination and suppression in the communication system, aiming to effectively reduce the impact of interference on system performance by optimizing resource allocation and signal processing strategy. The research follows from theoretical modeling, algorithm design and experimental verification, and proposes a dynamic interference coordination mechanism based on machine learning, which can adaptively adjust power allocation and frequency planning according to real-time channel state information, so as to significantly improve the spectrum efficiency and user fairness of the system. At the same time, for the strong interference scenario, this paper introduces the hybrid suppression technology of the joint airspace and the time-frequency domain, which further enhances the anti-interference ability of the system.
Keyword:Interference Coordination Machine Learning Spectrum Efficiency
目 录
1绪论 1
1.1通信系统干扰问题的背景与意义 1
1.2干扰协调与抑制方法的研究现状 1
1.3本文研究方法与技术路线 2
2干扰协调的基本理论与方法 2
2.1干扰协调的概念与分类 2
2.2干扰协调的数学建模分析 3
2.3基于资源分配的干扰协调方法 3
2.4干扰协调在多用户场景中的应用 4
3干扰抑制的关键技术与实现 4
3.1干扰抑制的技术原理概述 4
3.2基于滤波器设计的干扰抑制方法 5
3.3自适应干扰抑制算法的研究 5
3.4实时干扰抑制系统的性能评估 6
4干扰协调与抑制的综合优化策略 6
4.1干扰协调与抑制的联合优化框架 6
4.2基于机器学习的干扰管理方法 7
4.3跨层优化在干扰管理中的应用 7
4.4干扰管理策略的实际案例分析 8
结论 8
参考文献 10
致谢 11
