当前位置: X-MOL 学术IEEE Trans. Cognit. Commun. Netw. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous Multi-RAT Networks
IEEE Transactions on Cognitive Communications and Networking ( IF 8.6 ) Pub Date : 2022-03-02 , DOI: 10.1109/tccn.2022.3155727
Mhd Saria Allahham 1 , Alaa Awad Abdellatif 2 , Naram Mhaisen 3 , Amr Mohamed 2 , Aiman Erbad 4 , Mohsen Guizani 5
Affiliation  

The rapid production of mobile devices along with the wireless applications boom is continuing to evolve daily. This motivates the exploitation of wireless spectrum using multiple Radio Access Technologies (multi-RAT) and developing innovative network selection techniques to cope with such intensive demand while improving Quality of Service (QoS). Thus, we propose a distributed framework for dynamic network selection at the edge level, and resource allocation at the Radio Access Network (RAN) level, while taking into consideration diverse applications’ characteristics. In particular, our framework employs a deep Multi-Agent Reinforcement Learning (DMARL) algorithm, that aims to maximize the edge nodes’ quality of experience while extending the battery lifetime of the nodes and leveraging adaptive compression schemes. Indeed, our framework enables data transfer from the network’s edge nodes, with multi-RAT capabilities, to the cloud in a cost and energy-efficient manner, while maintaining QoS requirements of different supported applications. Our results depict that our solution outperforms state-of-the-art techniques of network selection in terms of energy consumption, latency, and cost.

中文翻译:

用于异构多​​ RAT 网络中网络选择和资源分配的多智能体强化学习

移动设备的快速生产以及无线应用的繁荣每天都在不断发展。这激发了使用多种无线电接入技术(multi-RAT)来开发无线频谱,并开发创新的网络选择技术来应对这种密集的需求,同时提高服务质量(QoS)。因此,我们提出了一种分布式框架,用于在边缘级别进行动态网络选择,并在无线接入网络(RAN)级别进行资源分配,同时考虑到不同应用程序的特性。特别是,我们的框架采用了深度多智能体强化学习 (DMARL) 算法,旨在最大限度地提高边缘节点的体验质量,同时延长节点的电池寿命并利用自适应压缩方案。的确,我们的框架支持从具有多 RAT 功能的网络边缘节点以成本和节能的方式将数据传输到云,同时保持不同支持的应用程序的 QoS 要求。我们的结果表明,我们的解决方案在能耗、延迟和成本方面优于最先进的网络选择技术。
更新日期:2022-03-02
down
wechat
bug