AI Based Power Allocation for NOMA

Talgat Manglayev, Refik Caglar Kizilirmak, Yau Hee Kho, Nor Asilah Wati Abdul Hamid, Yue Tian

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Novel methods using artificial intelligence for downlink power allocation problem in non-orthogonal multiple access networks are presented. The proposed machine learning and deep learning based methods achieved performance close to the optimum in terms of sum capacity with significantly lower computational costs. The numerical results also demonstrated up to 120 times a boost in computation time as compared to the conventional exhaustive search approach. Furthermore, the training and testing accuracy of the deep learning model reached 0.92 and 0.93 with the loss value dropping up to 0.002.

Original languageEnglish
Pages (from-to)3253-3261
Number of pages9
JournalWireless Personal Communications
Volume124
Issue number4
DOIs
Publication statusPublished - Jun 2022

Keywords

  • 5G
  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Non-orthogonal multiple access (NOMA)
  • Power allocation

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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