Reinforcement Learning Based Routing for Deadline-Driven Wireless Communication

Kiril Danilchenko; Gil Kedar; Michael Segal

doi:https://doi.org/10.1109/WiMob58348.2023.10187824

Reinforcement Learning Based Routing for Deadline-Driven Wireless Communication

Kiril Danilchenko, Gil Kedar, Michael Segal

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The objective of our work is to address the challenge of delivering time-sensitive data across a multi-hop wireless network. We aim to maximize the number of packets that reach their destination before the strict deadline. To achieve this, we introduce a deep reinforcement learning (DRL) approach that determines the optimal route, scheduling, and power allocation for each flow while complying with strict time constraints.

Original language	American English
Title of host publication	2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023
Pages	30-35
Number of pages	6
ISBN (Electronic)	9798350336672
DOIs	https://doi.org/10.1109/WiMob58348.2023.10187824
State	Published - 1 Jan 2023
Event	19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023 - Montreal, Canada Duration: 21 Jun 2023 → 23 Jun 2023

Publication series

Name	International Conference on Wireless and Mobile Computing, Networking and Communications
Volume	2023-June

Conference

Conference	19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023
Country/Territory	Canada
City	Montreal
Period	21/06/23 → 23/06/23

Keywords

Dynamic Power Assignment
Reinforcement Learning
Routing

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture
Software

Access to Document

https://doi.org/10.1109/WiMob58348.2023.10187824

Cite this

Danilchenko, K., Kedar, G., & Segal, M. (2023). Reinforcement Learning Based Routing for Deadline-Driven Wireless Communication. In 2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023 (pp. 30-35). (International Conference on Wireless and Mobile Computing, Networking and Communications; Vol. 2023-June). https://doi.org/10.1109/WiMob58348.2023.10187824

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title = "Reinforcement Learning Based Routing for Deadline-Driven Wireless Communication",

abstract = "The objective of our work is to address the challenge of delivering time-sensitive data across a multi-hop wireless network. We aim to maximize the number of packets that reach their destination before the strict deadline. To achieve this, we introduce a deep reinforcement learning (DRL) approach that determines the optimal route, scheduling, and power allocation for each flow while complying with strict time constraints.",

keywords = "Dynamic Power Assignment, Reinforcement Learning, Routing",

author = "Kiril Danilchenko and Gil Kedar and Michael Segal",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023 ; Conference date: 21-06-2023 Through 23-06-2023",

year = "2023",

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Danilchenko, K, Kedar, G & Segal, M 2023, Reinforcement Learning Based Routing for Deadline-Driven Wireless Communication. in 2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023. International Conference on Wireless and Mobile Computing, Networking and Communications, vol. 2023-June, pp. 30-35, 19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023, Montreal, Canada, 21/06/23. https://doi.org/10.1109/WiMob58348.2023.10187824

Reinforcement Learning Based Routing for Deadline-Driven Wireless Communication. / Danilchenko, Kiril; Kedar, Gil; Segal, Michael.
2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023. 2023. p. 30-35 (International Conference on Wireless and Mobile Computing, Networking and Communications; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Segal, Michael

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N2 - The objective of our work is to address the challenge of delivering time-sensitive data across a multi-hop wireless network. We aim to maximize the number of packets that reach their destination before the strict deadline. To achieve this, we introduce a deep reinforcement learning (DRL) approach that determines the optimal route, scheduling, and power allocation for each flow while complying with strict time constraints.

AB - The objective of our work is to address the challenge of delivering time-sensitive data across a multi-hop wireless network. We aim to maximize the number of packets that reach their destination before the strict deadline. To achieve this, we introduce a deep reinforcement learning (DRL) approach that determines the optimal route, scheduling, and power allocation for each flow while complying with strict time constraints.

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M3 - Conference contribution

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EP - 35

BT - 2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023

T2 - 19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023

Y2 - 21 June 2023 through 23 June 2023

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Danilchenko K, Kedar G, Segal M. Reinforcement Learning Based Routing for Deadline-Driven Wireless Communication. In 2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023. 2023. p. 30-35. (International Conference on Wireless and Mobile Computing, Networking and Communications). doi: https://doi.org/10.1109/WiMob58348.2023.10187824

Reinforcement Learning Based Routing for Deadline-Driven Wireless Communication

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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