摘 要
随着通信技术的快速发展，资源分配与调度算法在提升网络性能和用户体验方面发挥着至关重要的作用。本研究旨在针对当前通信网络中资源分配效率低、公平性不足以及动态适应能力有限的问题，提出一种基于多目标优化的资源分配与调度框架。通过结合机器学习预测模型与强化学习决策机制，该框架能够实时感知网络状态并动态调整资源分配策略，从而在满足用户服务质量需求的同时最大化系统整体效用。研究采用仿真平台对所提算法进行验证，结果表明，与传统静态分配方法相比，新算法在吞吐量、延迟和公平性等关键指标上均有显著提升。特别是在高负载场景下，其吞吐量提高了约30%，平均延迟降低了25%，且用户间资源分配的均衡性得到明显改善。
关键词：资源分配 多目标优化 机器学习
Abstract
With the rapid development of communication technology, resource allocation and scheduling algorithms play a crucial role in improving network performance and user experience. This study aims to propose a resource allocation and scheduling fr amework based on multi-ob jective optimization for the problems of low resource allocation efficiency, insufficient fairness and limited dynamic adaptability in the current communication network. By combining machine learning prediction model and reinforcement learning decision mechanism, this fr amework can sense the network state in real time and dynamically adjust the resource allocation strategy, so as to maximize the overall utility of the system while meeting users' service quality needs. The simulation platform was used to verify the proposed algorithm, and the results show that compared with the traditional static allocation method, the new algorithm has a significant improvement in throughput, delay and fairness. Especially in the high-load scenario, the throughput increases by about 30%, the average latency decreases by 25%, and the balance of resource allocation among users is significantly improved.
Keyword:Resource Allocation Multi-ob jective Optimization Machine Learning
目 录
1绪论 1
1.1通信网络资源分配的研究背景 1
1.2资源分配与调度算法的意义分析 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2资源分配的基础理论与模型 2
2.1通信网络资源分配的基本概念 2
2.2资源分配的数学建模方法 3
2.3典型资源分配算法的分类与特点 3
2.4算法性能评价指标体系 4
2.5基础理论在实际场景中的应用 4
3调度算法的设计与优化策略 4
3.1调度算法的核心设计原则 5
3.2动态调度算法的关键技术分析 5
3.3面向多用户的公平性调度策略 5
3.4基于机器学习的智能调度方法 6
3.5调度算法复杂度与效率权衡 6
4实验验证与案例分析 7
4.1实验环境与数据集构建 7
4.2不同算法的性能对比实验 7
4.3典型应用场景下的算法验证 8
4.4参数敏感性分析与优化建议 8
4.5实验结果总结与未来展望 9
结论 9
参考文献 11
致谢 12
随着通信技术的快速发展，资源分配与调度算法在提升网络性能和用户体验方面发挥着至关重要的作用。本研究旨在针对当前通信网络中资源分配效率低、公平性不足以及动态适应能力有限的问题，提出一种基于多目标优化的资源分配与调度框架。通过结合机器学习预测模型与强化学习决策机制，该框架能够实时感知网络状态并动态调整资源分配策略，从而在满足用户服务质量需求的同时最大化系统整体效用。研究采用仿真平台对所提算法进行验证，结果表明，与传统静态分配方法相比，新算法在吞吐量、延迟和公平性等关键指标上均有显著提升。特别是在高负载场景下，其吞吐量提高了约30%，平均延迟降低了25%，且用户间资源分配的均衡性得到明显改善。
关键词：资源分配 多目标优化 机器学习
Abstract
With the rapid development of communication technology, resource allocation and scheduling algorithms play a crucial role in improving network performance and user experience. This study aims to propose a resource allocation and scheduling fr amework based on multi-ob jective optimization for the problems of low resource allocation efficiency, insufficient fairness and limited dynamic adaptability in the current communication network. By combining machine learning prediction model and reinforcement learning decision mechanism, this fr amework can sense the network state in real time and dynamically adjust the resource allocation strategy, so as to maximize the overall utility of the system while meeting users' service quality needs. The simulation platform was used to verify the proposed algorithm, and the results show that compared with the traditional static allocation method, the new algorithm has a significant improvement in throughput, delay and fairness. Especially in the high-load scenario, the throughput increases by about 30%, the average latency decreases by 25%, and the balance of resource allocation among users is significantly improved.
Keyword:Resource Allocation Multi-ob jective Optimization Machine Learning
目 录
1绪论 1
1.1通信网络资源分配的研究背景 1
1.2资源分配与调度算法的意义分析 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2资源分配的基础理论与模型 2
2.1通信网络资源分配的基本概念 2
2.2资源分配的数学建模方法 3
2.3典型资源分配算法的分类与特点 3
2.4算法性能评价指标体系 4
2.5基础理论在实际场景中的应用 4
3调度算法的设计与优化策略 4
3.1调度算法的核心设计原则 5
3.2动态调度算法的关键技术分析 5
3.3面向多用户的公平性调度策略 5
3.4基于机器学习的智能调度方法 6
3.5调度算法复杂度与效率权衡 6
4实验验证与案例分析 7
4.1实验环境与数据集构建 7
4.2不同算法的性能对比实验 7
4.3典型应用场景下的算法验证 8
4.4参数敏感性分析与优化建议 8
4.5实验结果总结与未来展望 9
结论 9
参考文献 11
致谢 12
