摘 要：
随着互联网的快速发展和网络流量的持续增长，传统基于规则的拥塞控制算法逐渐难以适应复杂多变的网络环境。为此，本研究提出了一种基于机器学习的新型网络拥塞控制算法，旨在通过数据驱动的方式提升网络性能与用户体验。研究首先分析了现有拥塞控制算法在动态网络条件下的局限性，并结合机器学习技术的优势，设计了一种融合深度强化学习与自适应控制机制的算法框架。该算法能够实时感知网络状态并动态调整传输速率，从而有效应对复杂的网络拥塞问题。实验采用真实网络环境与仿真平台相结合的方式，对所提算法进行了全面验证。结果表明，相较于传统算法（如TCP Cubic）及部分基于机器学习的改进算法，本研究提出的算法在吞吐量、延迟和丢包率等关键指标上均表现出显著优势。此外，该算法具备较强的鲁棒性和可扩展性，能够在多种网络场景下保持稳定性能。本研究的主要创新点在于首次将深度强化学习引入拥塞控制领域，并通过优化模型结构与训练策略，显著降低了计算复杂度与资源消耗。这一成果为未来智能化网络管理提供了新的思路与技术支撑，同时为机器学习在通信领域的应用开辟了更广阔的空间。
关键词：网络拥塞控制；机器学习；深度强化学习；自适应控制；算法性能优化
Research on Network Congestion Control Algorithms Based on Machine Learning
Abstract: With the rapid development of the Internet and the continuous growth of network traffic, traditional rule-based congestion control algorithms are gradually struggling to adapt to complex and dynamic network environments. To address this challenge, this study proposes a novel machine-learning-based network congestion control algorithm that aims to enhance network performance and user experience through a data-driven approach. The research first analyzes the limitations of existing congestion control algorithms under dynamic network conditions and leverages the advantages of machine learning technologies to design an algorithm fr amework that integrates deep reinforcement learning with adaptive control mechanisms. This algorithm is capable of real-time network state perception and dynamic adjustment of transmission rates, effectively addressing complex congestion issues. Experiments were conducted using a combination of real-world network environments and simulation platforms to comprehensively validate the proposed algorithm. The results demonstrate that, compared with traditional algorithms (e.g., TCP Cubic) and some machine-learning-based improved algorithms, the proposed algorithm exhibits significant advantages in key metrics such as throughput, latency, and packet loss rate. Additionally, the algorithm shows strong robustness and scalability, maintaining stable performance across various network scenarios. A major innovation of this study lies in the first application of deep reinforcement learning to the field of congestion control, along with optimizations in model structure and training strategies that substantially reduce computational complexity and resource consumption. This achievement provides new insights and technical support for future intelligent network management and opens up broader possibilities for the application of machine learning in communication domains.
Keywords: Network Congestion Control; Machine Learning; Deep Reinforcement Learning; Adaptive Control; Algorithm Performance Optimization
目 录
1绪论 1
1.1网络拥塞控制的研究背景 1
1.2机器学习在拥塞控制中的意义 1
1.3当前研究现状与挑战 1
1.4本文研究方法概述 2
2机器学习基础与算法适配性分析 2
2.1机器学习的核心原理简介 2
2.2常见机器学习算法的特性对比 3
2.3拥塞控制对机器学习算法的需求 3
2.4算法适配性评估框架构建 4
2.5适配性分析案例研究 4
3基于机器学习的拥塞控制算法设计 5
3.1拥塞控制问题的形式化定义 5
3.2数据驱动的拥塞状态预测模型 5
3.3强化学习在拥塞控制中的应用 6
3.4算法优化策略与参数调优 6
3.5设计方案的可行性验证 7
4算法性能评估与实际应用分析 7
4.1性能评估指标体系构建 7
4.2实验环境与数据集选择 8
4.3不同场景下的算法表现分析 8
4.4与传统算法的对比实验结果 9
4.5实际部署中的问题与改进方向 9
结论 10
参考文献 11
致 谢 12
