Multi-agent reinforcement learning based 5G bi-level multi-slice resource allocation

Zhipeng Yu, Fangqing Gu
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Abstract

With the complexity of application scenarios and the wide application of network slicing, the base station faces different requirements of different slicing in resource allocation. The base station can separate the centralized unit (CU)-distributed unit (DU) according to the slicing demand and carry out different charges according to different separation schemes. We build a more efficient bi-level model for resource allocation in the context of CU-DU separation, and for practical consideration, we use the deterministic reinforcement learning algorithm to solve this bi-level model. The results show that the reinforcement learning method is effective in solving the bi-level resource allocation model.
基于多智能体强化学习的5G双层多片资源分配
随着应用场景的复杂性和网络切片的广泛应用,基站在资源分配上面临着不同切片的不同要求。基站可以根据切片需求将集中式单元(CU)和分布式单元(DU)分离,并根据不同的分离方案进行不同的收费。在CU-DU分离的背景下,我们建立了一个更高效的资源分配双层模型,并考虑到实际情况,我们使用确定性强化学习算法来求解这个双层模型。结果表明,强化学习方法是求解双层资源分配模型的有效方法。
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