Power Allocation for NOMA-Based Visible Light Communication Systems with DQN

  • Jiawei Deng*
  • , Xuan Tang*
  • , Xian Wei
  • , Pu Li*
  • , Jiaqi Li*
  • , Xicong Li
  • , Zabih Ghassemlooy
  • *Corresponding author for this work

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

1 Scopus citations

Abstract

The spectral efficiency of the visible light communication system can be enhanced by using the non-orthogonal multiple access (NOMA) scheme. In this paper, we propose a deep Q network (DQN) framework-based power allocation scheme that maximizes the sum rate of a NOMA-based cellular VLC network with mobility support. The numerical results indicate that the optimisation process achieves improved performance in terms of sum data rate (SDR) by approximately 8.8, 7, and 4% compared with conventional algorithms, such as fixed power allocation, gain ratio power allocation, and genetic algorithms.

Original languageEnglish
Title of host publication2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages512-517
Number of pages6
ISBN (Electronic)9798350348743
DOIs
StatePublished - 2024
Event14th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2024 - Rome, Italy
Duration: 17 Jul 202419 Jul 2024

Publication series

Name2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2024

Conference

Conference14th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2024
Country/TerritoryItaly
CityRome
Period17/07/2419/07/24

Keywords

  • Deep reinforcement learning
  • NOMA
  • power allocation
  • visible light communication

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