Reinforcement Learning Based Routing for Deadline-Driven Wireless Communication

Kiril Danilchenko, Gil Kedar, Michael Segal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publication2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023
PublisherInstitute of Electrical and Electronics Engineers
Pages30-35
Number of pages6
ISBN (Electronic)9798350336672
DOIs
StatePublished - 1 Jan 2023
Event19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023 - Montreal, Canada
Duration: 21 Jun 202323 Jun 2023

Publication series

NameInternational Conference on Wireless and Mobile Computing, Networking and Communications
Volume2023-June
ISSN (Print)2161-9646
ISSN (Electronic)2161-9654

Conference

Conference19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023
Country/TerritoryCanada
CityMontreal
Period21/06/2323/06/23

Keywords

  • Dynamic Power Assignment
  • Reinforcement Learning
  • Routing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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