摘 要
随着5G网络的快速发展，其低时延、高带宽和大连接数的特点为移动边缘计算（MEC）提供了广阔的应用空间。本研究旨在探讨MEC在5G网络中的应用，以解决传统云计算模式下数据传输延迟高、带宽占用大等问题。通过将计算资源部署在网络边缘，MEC能够显著降低终端设备与服务器之间的通信时延，提高数据处理效率。本文采用理论分析与仿真模拟相结合的方法，构建了基于MEC架构的5G网络模型，并对其性能进行了全面评估。研究表明，在智能交通系统中，MEC可实现车辆与基础设施间的实时信息交互，有效提升交通安全性和通行效率；在工业互联网领域，MEC支持工厂内大量传感器数据的本地化处理，减少了云端传输压力并加快了响应速度。此外，针对MEC面临的资源分配、安全隐私等挑战，提出了基于深度强化学习的动态资源调度算法及差分隐私保护机制，确保了系统的高效稳定运行。该研究不仅验证了MEC技术在5G网络环境下的可行性与优越性，还为未来6G网络的发展奠定了理论基础，具有重要的学术价值和实际应用前景。
关键词：移动边缘计算 5G网络 低时延高带宽
Abstract
With the rapid development of 5G networks, their characteristics of low latency, high bandwidth, and massive connectivity provide extensive application prospects for mobile edge computing (MEC). This study aims to explore the application of MEC in 5G networks to address issues such as high data transmission delays and substantial bandwidth consumption inherent in traditional cloud computing models. By deploying computational resources at the network edge, MEC can significantly reduce communication latency between terminal devices and servers, thereby enhancing data processing efficiency. Utilizing a combination of theoretical analysis and simulation modeling, this paper constructs a 5G network model based on MEC architecture and conducts a comprehensive evaluation of its performance. The research indicates that in intelligent transportation systems, MEC enables real-time information exchange between vehicles and infrastructure, effectively improving traffic safety and efficiency; in the field of industrial internet, MEC supports localized processing of large amounts of sensor data within factories, reducing pressure on cloud transmission and accelerating response times. Furthermore, addressing challenges related to resource allocation and security privacy in MEC, this study proposes a dynamic resource scheduling algorithm based on deep reinforcement learning and a differential privacy protection mechanism, ensuring efficient and stable system operation. This research not only verifies the feasibility and superiority of MEC technology in 5G network environments but also lays a theoretical foundation for the development of future 6G networks, possessing significant academic value and practical application prospects.
Keyword:Mobile Edge Computing 5G Network Low Latency High Bandwidth
目 录
引言 1
1移动边缘计算概述 1
1.1移动边缘计算概念 1
1.3边缘计算与 2
2关键技术支撑 3
2.1网络架构设计 3
2.2资源分配策略 3
2.3数据处理机制 4
3应用场景分析 4
3.1智能交通系统 5
3.2工业互联网应用 5
3.3增强现实体验 6
4面临挑战及对策 6
4.1安全性保障措施 6
4.2标准化建设路径 7
4.3商业模式探索 7
结论 8
参考文献 9
致谢 9
