عرض بسيط للتسجيلة

المؤلفAllahham, Mhd Saria
المؤلفAbdellatif, Alaa Awad
المؤلفMhaisen, Naram
المؤلفMohamed, Amr
المؤلفErbad, Aiman
المؤلفGuizani, Mohsen
تاريخ الإتاحة2022-10-03T20:27:09Z
تاريخ النشر2022-06-01
اسم المنشورIEEE Transactions on Cognitive Communications and Networking
المعرّفhttp://dx.doi.org/10.1109/TCCN.2022.3155727
الاقتباسAllahham, M. S., Abdellatif, A. A., Mhaisen, N., Mohamed, A., Erbad, A., & Guizani, M. (2022). Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous multi-RAT Networks. IEEE Transactions on Cognitive Communications and Networking.‏
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125734785&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/34763
الملخصThe 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعdeep reinforcement learning
edge computing
Heterogeneous networks
multi-RAT architecture
wireless healthcare systems
العنوانMulti-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous Multi-RAT Networks
النوعArticle
الصفحات1287-1300
رقم العدد2
رقم المجلد8


الملفات في هذه التسجيلة

Thumbnail

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة