A Multi-Agent Proximal Policy Optimized Joint Mechanism in mmWave HetNets With CoMP Toward Energy Efficiency Maximization

IF 5.3 2区计算机科学 Q1 TELECOMMUNICATIONS

IEEE Transactions on Green Communications and Networking Pub Date : 2023-11-20 DOI:10.1109/TGCN.2023.3334495

Amin Lotfolahi;Huei-Wen Ferng

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Abstract

A novel cluster-based traffic offloading and user association (UA) algorithm alongside a multi-agent deep reinforcement learning (DRL) based base station (BS) activation mechanism is proposed in this paper. Our design aims to maximize the energy efficiency (EE) of the heterogeneous network (HetNet) while maintaining high quality of service (QoS). By taking advantage of the dense deployment of BSs in a HetNet, a clustering algorithm is first proposed to facilitate traffic offloading among BSs. Then, a multi-agent proximal policy optimization (MAPPO) based DRL algorithm is employed to trigger the BS activation decision based on the current environmental condition. Finally, a UA algorithm is deployed to improve further the (normalized) data rate of all users, known as the (normalized) sum rate. Via simulation, we show that our proposed mechanism can remarkably enhance the EE and excel over the closely related mechanisms. It satisfies the required data rate, improves the sum rate, and exhibits excellent scalability when many BSs are deployed.

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具有 CoMP 的毫米波 HetNets 中的多代理近端策略优化联合机制，以实现能效最大化

本文提出了一种新颖的基于集群的流量卸载和用户关联（UA）算法，以及一种基于多代理深度强化学习（DRL）的基站（BS）激活机制。我们的设计旨在最大限度地提高异构网络（HetNet）的能效（EE），同时保持较高的服务质量（QoS）。利用 HetNet 中密集部署 BS 的优势，本文首先提出了一种聚类算法，以促进 BS 之间的流量卸载。然后，采用基于多代理近端策略优化（MAPPO）的 DRL 算法，根据当前环境条件触发 BS 激活决策。最后，采用 UA 算法进一步提高所有用户的（归一化）数据传输速率，即（归一化）总速率。通过仿真，我们发现我们提出的机制可以显著提高 EE，并优于其他密切相关的机制。它满足了所需的数据速率，提高了总和速率，并在部署多个 BS 时表现出卓越的可扩展性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Green Communications and Networking Computer Science-Computer Networks and Communications

CiteScore

9.30

自引率

6.20%

发文量

181

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