TDMA Frame Length Minimization by Deep Learning for Swarm Communication.

Kiril Danilchenko, Michael Segal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this study, we consider a scenario where Unmanned Aerial Vehicles are deployed in an area of interest in order to monitor this area, and the data gathering process following the monitoring is applied towards a central UAV (sink) for analysis. Our objective is to find a minimum-length TDMA schedule by jointly determining the scheduled concurrent links at each slot, along with their used power levels and the established routes toward the sink, under SINR requirements. Since the problem is known to be NP-hard, we aim to provide solutions to real-world situations using efficient heuristic approach. Our research goal is to use deep neural network to approximate the solution of finding the minimum frame length for the TDMA frame. We propose the TDMA frame length minimization by the deep neural network approach (named MTFLet). We have performed extensive experimental evaluations of MTFLet and simulations showing that MTFLet outperforms other state-of-art methods.
Original languageEnglish
Title of host publication2022 International Wireless Communications and Mobile Computing (IWCMC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages617-622
Number of pages6
ISBN (Electronic)9781665467490
DOIs
StatePublished - 2022
Event18th IEEE International Wireless Communications and Mobile Computing, IWCMC 2022 - Dubrovnik, Croatia
Duration: 30 May 20223 Jun 2022

Publication series

Name2022 International Wireless Communications and Mobile Computing, IWCMC 2022

Conference

Conference18th IEEE International Wireless Communications and Mobile Computing, IWCMC 2022
Country/TerritoryCroatia
CityDubrovnik
Period30/05/223/06/22

Keywords

  • Deep neural network
  • Convergen- cast , UAVs
  • Scheduling
  • Deep learning
  • Wireless communication
  • Time division multiple access

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