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GenNet: A Generative AI-Driven Mobile Network Simulator for Multi-Objective Network Optimization With RL

delete2025-11-26
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PRE
AI
W
Weikang Su
T
Tong Li
W
Wenzhen Huang
Z
Z. Huang
Y
Yong Li
DOI：10.1109/TMC.2025.3637214delete
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摘要

摘要

En 中文
基于仿真的优化已成为移动网络优化领域的关键方法，用于解决动态和预测性网络管理的需求。为解决高级研究对开源移动网络仿真器的短缺问题，我们开发了GenNet——一个由生成式AI驱动的移动网络仿真器。GenNet能够创建移动用户、基站和无线环境的虚拟副本，利用生成式AI方法在多种网络设置下高精度模拟这些实体的行为。GenNet配备专门为强化学习环境设计的定制API，使研究人员能够精细调整网络参数（如倾角、方位角和发射功率）。大量实验利用GenNet对多目标优化算法进行基准测试，重点关注增强网络覆盖、吞吐量和能效，从而验证其作为推动网络优化技术的强大平台的效能。通过这一创新工具，我们旨在赋能研究人员和从业者识别并实施最有效的网络优化方法，为移动网络管理的未来发展铺平道路。
Keyword:
Mobile network simulator
generative AI
multi-objective optimization
reinforcement learning

期刊

IEEE Transactions on Mobile Computing 封面图
IEEE Transactions on Mobile Computing
IF:
9.2
论文数:
5.8K
被引数:
1.8W

机构

T
tsinghua university
学者数:
11.9W
论文数: 10.0W
被引数: 137
引用论文

引用论文

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