摘 要
随着5G及未来6G通信技术的快速发展，毫米波频段因其丰富的频谱资源成为提升系统容量和传输速率的关键手段，但同时也面临频谱分配效率低、干扰管理复杂等挑战。本研究聚焦毫米波通信中的频谱资源管理与优化问题，旨在提出高效可行的解决方案以提升系统性能。通过引入先进的数学建模方法和优化算法，研究设计了一种基于机器学习的动态频谱分配策略，能够适应复杂多变的网络环境并显著降低用户间干扰。此外，创新性地结合博弈论思想，提出了一种分布式资源协调机制，有效平衡了全局性能与计算复杂度之间的矛盾。仿真结果表明，所提方法在频谱利用率和系统吞吐量方面较传统方案分别提升了约30%和40%，同时大幅减少了算法运行时间。研究表明，该研究为毫米波通信系统的频谱管理提供了新思路，对实际工程应用具有重要参考价值。
关键词：毫米波通信；频谱资源管理；机器学习
Abstract
With the rapid development of 5G and future 6G communication technologies, the millimeter-wave band has become a critical means to enhance system capacity and transmission rates due to its abundant spectrum resources. However, it also faces challenges such as low spectrum allocation efficiency and complex interference management. This study focuses on spectrum resource management and optimization in millimeter-wave communications, aiming to propose effective and feasible solutions to improve system performance. By incorporating advanced mathematical modeling methods and optimization algorithms, a machine-learning-based dynamic spectrum allocation strategy is designed, which can adapt to complex and changing network environments while significantly reducing user interference. Furthermore, by innovatively integrating game-theoretic concepts, a distributed resource coordination mechanism is proposed, effectively balancing the trade-off between global performance and computational complexity. Simulation results demonstrate that the proposed method improves spectrum utilization and system throughput by approximately 30% and 40%, respectively, compared to traditional schemes, while substantially reducing algorithm execution time. This research provides new insights into spectrum management for millimeter-wave communication systems and offers significant reference value for practical engineering applications.
Keywords: Millimeter Wave Communication；Spectrum Resource Management；Machine Learning
目 录
引言 1
一、毫米波通信基础与挑战 1
（一）毫米波频谱特性分析 1
（二）通信系统关键挑战探讨 2
（三）技术背景与发展现状 2
二、频谱资源分配策略研究 3
（一）动态频谱分配机制设计 3
（二）分布式资源管理方法 3
（三）频谱效率优化技术 3
三、干扰管理与性能优化 4
（一）干扰建模与分析方法 4
（二）空间复用与干扰协调 4
（三）性能优化算法设计 5
四、实时优化与应用实践 5
（一）实时频谱调度方案 5
（二）网络部署与实验验证 6
（三）应用场景与未来方向 6
结 论 6
致 谢 8
参考文献 9
