摘 要
随着移动通信技术的快速发展，5G无线网络已成为推动数字化转型和智能化社会建设的核心基础设施。本研究以提升5G网络性能为目标，深入探讨了其关键技术及优化策略，重点分析了大规模天线阵列、超密集组网、边缘计算以及新型多址接入等核心技术在实际部署中的应用效果。通过构建仿真模型并结合真实场景数据，研究提出了一种基于机器学习的动态资源分配算法，能够显著提高频谱效率和能量效率，同时降低网络时延。此外，针对超密集网络中干扰管理问题，设计了一种自适应干扰协调机制，有效缓解了小区间干扰对系统性能的影响。研究结果表明，所提出的优化方法能够在复杂环境下实现网络性能的全面提升，特别是在高负载和高频段场景下表现出优异的稳定性与适应性。本研究的主要创新点在于将人工智能技术与传统通信理论深度融合，为5G网络的智能化运维提供了新思路，并为未来6G技术的发展奠定了理论基础。最终结论显示，通过关键技术的协同优化，可以进一步挖掘5G网络潜力，满足日益增长的用户需求和服务要求，从而为智慧城市建设和社会经济发展提供强有力的技术支撑。
关键词：5G网络；机器学习；动态资源分配；自适应干扰协调；超密集组网
With the rapid development of mobile communication technology, 5G wireless networks have become a core infrastructure for driving digital transformation and constructing intelligent societies. This study aims to enhance the performance of 5G networks by thoroughly investigating its key technologies and optimization strategies, with a focus on the practical application effects of large-scale antenna arrays, ultra-dense networking, edge computing, and novel multiple access techniques. By constructing simulation models and integrating real-world scenario data, a machine-learning-based dynamic resource allocation algorithm is proposed, which significantly improves spectral efficiency and energy efficiency while reducing network latency. Additionally, an adaptive interference coordination mechanism is designed to address interference management issues in ultra-dense networks, effectively mitigating the impact of inter-cell interference on system performance. The results demonstrate that the proposed optimization methods can comprehensively improve network performance in complex environments, particularly exhibiting superior stability and adaptability under high-load and high-frequency scenarios. The primary innovation of this study lies in the deep integration of artificial intelligence technologies with traditional communication theories, providing new insights into the intelligent operation and maintenance of 5G networks and laying a theoretical foundation for the future development of 6G technologies. The final conclusion indicates that through collaborative optimization of key technologies, the potential of 5G networks can be further unlocked to meet the growing demands for user services, thereby offering strong technical support for smart city construction and socio-economic development.
Keywords: 5G Network; Machine Learning; Dynamic Resource Allocation; Adaptive Interference Coordination; Ultra Dense Networking
目 录
1绪论 1
1.15G无线网络研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
25G关键技术的理论基础 2
2.1大规模MIMO技术原理 2
2.2毫米波通信技术特性 3
2.3超密集网络部署策略 3
2.4边缘计算在5G中的应用 4
35G网络性能优化的核心问题 4
3.1网络能耗优化方法 4
3.2频谱效率提升策略 5
3.3数据传输延迟控制 5
3.4用户体验质量保障 6
45G关键技术的实际应用与挑战 7
4.1智能交通中的5G应用 7
4.2工业物联网场景分析 7
4.3安全性与隐私保护问题 8
4.4标准化与兼容性探讨 8
结论 9
参考文献 10
致 谢 11
