Transmission Power Control using Deep Neural Networks in TDMA-based Ad-hoc Network Clusters

Rina Azoulay, Kiril Danilchenko, Yoram Haddad, Shulamit Reches

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

3 Scopus citations

Abstract

In this work, we considered power allocation and request scheduling in mobile ad hoc networks (MANET) clusters, which are generally modeled as optimization problems with constraints. Optimization algorithms often incur significant time complexity, which creates significant discrepancies between theoretical results and real-time processing. We aim to provide a novel machine-learning-based perspective to address this challenge. We use a deep neural network (DNN) to approximate the nonlinear mapping between the inputs and outputs of an optimization algorithm. If a nonlinear mapping can be learned accurately by a DNN, then optimization tasks can be performed more efficiently. We propose SPCDNet as a scheduling and power control deep network mapping method. A key challenge in training a DNN for resource allocation problems is a lack of ground-truth data, meaning the optimal power allocation between the time slots of each transmitter is unknown. To address this issue, we designed an optimal solver based on linear programming optimization methods and used its solutions to train SPCDNet. Simulation results demonstrate that SPCDNet can serve as a computationally inexpensive replacement for traditionally expensive optimization algorithms in real-time tasks and provide very good approximation solutions, where the average run time of SPCDNet for each network size is very low compared to the hundreds of seconds used by an optimal solver, whose time complexity increases exponentially with the input size.

Original languageEnglish
Title of host publication2021 International Wireless Communications and Mobile Computing, IWCMC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages406-411
Number of pages6
ISBN (Electronic)9781728186160
DOIs
StatePublished - 1 Jan 2021
Event17th IEEE International Wireless Communications and Mobile Computing, IWCMC 2021 - Virtual, Online, China
Duration: 28 Jun 20212 Jul 2021

Publication series

Name2021 International Wireless Communications and Mobile Computing, IWCMC 2021

Conference

Conference17th IEEE International Wireless Communications and Mobile Computing, IWCMC 2021
Country/TerritoryChina
CityVirtual, Online
Period28/06/212/07/21

Keywords

  • Ad hoc
  • Cluster
  • D2D
  • Deep neural network
  • Machine learning
  • Power control
  • Scheduling
  • TDMA
  • Wireless

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