Hopfield Neural Network based Uplink/Downlink Transmission Order Optimization for Dynamic Indoor TDD Femtocells

Mirza Nazrul Alam, Riku Jantti, Zekeriya Uykan

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
25 Downloads (Pure)

Abstract

The Uplink/Downlink transmission mode or Transmission Order (TO) optimization has recently appeared as a new optimization domain in radio resource management. Such optimization is a combinatorics problem and requires good heuristic algorithm to be approximately solved within short time for the dynamic radio environment. This paper shows how the TO optimization problem in Time Division Duplex (TDD) indoor femtocells can be formulated and solved by the Hopfield Neural Network (HNN) based TO schedulers. Both centralized and distributed versions are analyzed in the context of indoor femtocells. We also examine proposed TO schedulers' system performance in TDD indoor femtocells environment by extensive simulation campaigns. Our simulation results for a large 3-story building including 120 femtocells show that (i) the indoor femtocell system performance is improved up to 13 to 20 percent by the proposed HNN schedulers depending on the number of femtocells, (ii) the proposed TO schedulers converge within the first few epochs. (iii) The performance of the proposed schedulers are justified by a time-consuming but a thorough Genetic Algorithm Scheduler.

Original languageEnglish
Pages (from-to)85414-85425
Number of pages12
JournalIEEE Access
Volume11
Early online date2023
DOIs
Publication statusPublished - 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • Downlink
  • Dynamic-TDD
  • Femtocell networks
  • Femtocells
  • Genetic algorithm
  • Genetic algorithms
  • Hopfield Neural Network
  • Hopfield neural networks
  • Indoor communication
  • Interference
  • Neural networks
  • Optimization
  • Resource management
  • System performance
  • Transmission order optimization
  • Uplink
  • Wireless communication

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