Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous Multi-RAT Networks

Mhd Saria Allahham, Alaa Awad Abdellatif, Naram Mhaisen, Amr Mohamed*, Aiman Erbad, Mohsen Guizani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1287-1300
Number of pages14
JournalIEEE Transactions on Cognitive Communications and Networking
Volume8
Issue number2
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • Deep reinforcement learning
  • Edge computing
  • Heterogeneous networks
  • Wireless healthcare systems
  • multi-RAT architecture

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