Resilient Topology Design for Wireless Backhaul: A Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

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Resilient Topology Design for Wireless Backhaul: A Deep Reinforcement Learning Approach

Published:2022-12 Issue:12 Volume:11 Page:2532-2536
ISSN:2162-2337
Container-title:IEEE Wireless Communications Letters
language:
Short-container-title:IEEE Wireless Commun. Lett.

Author:

Abdelmoaty Ahmed¹^ORCID,Naboulsi Diala²^ORCID,Dahman Ghassan³^ORCID,Poitau Gwenael⁴^ORCID,Gagnon Francois⁵

Affiliation:

1. Electrical Engineering Department and the Resilient Machine Learning Institute, École de technologie supérieure, Montreal, QC, Canada

2. Software Engineering Department and IT and the Resilient Machine Learning Institute, École de technologie supérieure, Montreal, QC, Canada

3. Ultra Intelligence and Communications, Montreal, QC, Canada

4. Dell Technologies, Toronto, ON, Canada

5. Electrical Engineering Department, École de technologie supérieure, Montreal, QC, Canada

Funder

Ultra Intelligence and Communications Research Chair

NSERC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/5962382/9976385/09893897.pdf?arnumber=9893897

Reference13 articles.

1. A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

2. Optimizing C-RAN Backhaul Topologies: A Resilience-Oriented Approach Using Graph Invariants

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1. Resilient enhancements of routing protocols in MANET;Peer-to-Peer Networking and Applications;2024-06-26

2. Temporal-correlation Modeling for Improved CFO Estimation: The BiModule CFO Estimation (BMCE) Framework;2024 International Wireless Communications and Mobile Computing (IWCMC);2024-05-27

3. Deep Reinforcement Learning for Backhaul Link Selection for Network Slices in IAB Networks;GLOBECOM 2023 - 2023 IEEE Global Communications Conference;2023-12-04

4. When Resiliency Matters: An Overview of 5G and Beyond Wireless Backhaul Network Design;IEEE Communications Magazine;2023-12

5. Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues;Electricity;2023-12-01

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