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本项目的核心算法为基于深度强化学习的负载均衡模型,所对应的学术成果发表于IEEE Internet of Things Journal,题目为Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach。如果本项目的工作对您有参考价值,欢迎引用我们的成果,bib格式如下:
@Article{xu2019load,
title = {Load Balancing for UltraDense Networks: A Deep Reinforcement Learning Based Approach},
journal = {IEEE} Internet Things J.,
author = {Y. {Xu} and W. {Xu} and Z. {Wang} and J. {Lin} and S. {Cui}},
year = {2019},
volume = {6},
number = {6},
pages = {9399-9412},
month = {Dec.},
}
本代码库提供了该算法的demo,供测试使用。
本算法的目的是实现具有可拓展性的双层负载均衡框架,用于在超密集网络中以自组织管理的形式控制大规模基站间的负载均衡。该算法包含两个子算法:一个是位于上层的K-means聚类算法,目的是根据各个基站的历史负载波动情况将其划分到不同的基站聚类中;一个是位于下层的深度强化学习算法,目的是平衡各个基站聚类内的基站负载。
算法程序的入口为run_ddpg.py,深度强化学习相关算法存储在ddpg文件夹中;环境相关算法存储在env文件夹中;outputs用于存放程序输出的结果数据。在运行主程序run_ddpg.py前,需先运行env中的gen_cell_loc.py和gen_user_trajectory.py来生成基站和用户分布数据。具体的计算逻辑可参考程序注释和论文原文。
前沿的人工智能技术可以有效地推动下一代无线系统和网络的智能化演进。本平台旨在为相关工作者提供一个共享数据和代码的空间。
MATLAB Python CSV
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