摘 要：
随着无线通信技术的快速发展，资源分配与性能优化成为提升网络效率和用户体验的关键问题。本研究聚焦于无线网络中的资源分配策略及其对系统性能的影响，旨在提出一种高效、灵活且适应性强的优化方法以应对复杂多变的网络环境。通过对现有资源分配算法的深入分析，发现传统方法在动态场景下存在适应性不足、公平性欠缺以及能耗较高的局限性。为此，本文提出了一种基于机器学习的自适应资源分配框架，该框架结合强化学习与深度神经网络，能够根据实时网络状态动态调整资源分配方案。实验结果表明，所提方法在吞吐量、延迟和能耗等关键性能指标上均显著优于传统算法，尤其是在高负载和异构网络环境中表现出更强的鲁棒性和更高的资源利用率。此外，本文还引入了公平性评估机制，确保用户间的服务质量均衡，从而提升了整体用户体验。研究的主要贡献在于首次将自适应学习机制与无线资源管理深度融合，为未来智能化网络的设计提供了新的思路和技术支持。这一成果不仅有助于推动无线通信理论的发展，也为5G及后5G时代的网络优化实践提供了重要参考。
关键词：无线资源分配；机器学习；自适应优化；网络性能；公平性评估
Resource Allocation and Performance Optimization Strategies in Wireless Networks
Abstract: With the rapid development of wireless communication technologies, resource allocation and performance optimization have become critical issues for enhancing network efficiency and user experience. This study focuses on resource allocation strategies in wireless networks and their impact on system performance, aiming to propose an efficient, flexible, and adaptive optimization approach to address the complexities of dynamic network environments. Through a thorough analysis of existing resource allocation algorithms, it is found that traditional methods suffer from limitations such as insufficient adaptability in dynamic scenarios, lack of fairness, and high energy consumption. To address these challenges, this paper introduces a machine-learning-based adaptive resource allocation fr amework that integrates reinforcement learning with deep neural networks, enabling dynamic adjustment of resource allocation schemes according to real-time network states. Experimental results demonstrate that the proposed method significantly outperforms conventional algorithms in key performance metrics such as throughput, latency, and energy consumption, particularly exhibiting stronger robustness and higher resource utilization in high-load and heterogeneous network environments. Additionally, a fairness evaluation mechanism is incorporated to ensure balanced quality of service among users, thereby improving overall user experience. The primary contribution of this research lies in its pioneering integration of adaptive learning mechanisms with wireless resource management, offering novel insights and technical support for the design of future intelligent networks. This achievement not only advances the theoretical development of wireless communications but also provides significant references for network optimization practices in the 5G and post-5G eras.
Keywords: Wireless Resource Allocation; Machine Learning; Adaptive Optimization; Network Performance; Fairness Evaluation
目 录
1绪论 1
1.1无线网络资源分配的研究背景 1
1.2资源分配与性能优化的意义 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2无线网络资源分配的基础理论 2
2.1无线通信系统的基本原理 2
2.2资源分配的关键概念与模型 3
2.3性能优化的数学基础 3
2.4常见资源分配算法综述 4
2.5理论框架对研究的指导作用 4
3资源分配策略的设计与实现 5
3.1动态频谱分配策略研究 5
3.2功率控制在资源分配中的应用 6
3.3用户调度与带宽分配优化 6
3.4干扰管理与信道分配策略 7
3.5实验仿真与结果分析 7
4性能优化策略的评估与改进 8
4.1系统吞吐量优化方法 8
4.2延迟与可靠性优化策略 8
4.3能效与资源利用率提升方案 9
4.4多目标优化的权衡与实现 9
4.5实际场景中的性能验证 10
结论 10
参考文献 12
致 谢 13
