Subcarrier Allocation for Multiuser OFDM Systems by Using Deep Neural Networks

Jia Jhe Song*, Wei Jen Chen, Yung Fang Chen, Shu Ming Tseng

*Corresponding author for this work

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

Abstract

Previously, we proposed schemes in [1] and [2] for the classical subcarrier, bit, and power allocation problem [3] to minimize the total transmit power for multiuser orthogonal frequency division multiplexing systems in downlink transmission. In this paper, we propose a deep neural network (DNN) structure to speed up solving this complex problem. We propose a deep learning frame structure in which each group of allocation is termed as a batch; after some numbers of iterations and epochs, the loss will tend to converge to a constant value. The simulation results reveal that the proposed DNN-based schemes offer competitive performance and reduce computing time tremendously compared with those of the existing approaches.

Original languageEnglish
Title of host publication2023 9th International Conference on Applied System Innovation, ICASI 2023
EditorsShoou-Jinn Chang, Sheng-Joue Young, Artde Donald Kin-Tak Lam, Liang-Wen Ji, Stephen D. Prior
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages253-255
Number of pages3
ISBN (Electronic)9798350398380
DOIs
StatePublished - 2023
Externally publishedYes
Event9th International Conference on Applied System Innovation, ICASI 2023 - Chiba, Japan
Duration: 21 04 202325 04 2023

Publication series

Name2023 9th International Conference on Applied System Innovation, ICASI 2023

Conference

Conference9th International Conference on Applied System Innovation, ICASI 2023
Country/TerritoryJapan
CityChiba
Period21/04/2325/04/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • deep learning
  • deep neural networks
  • orthogonal frequency division multiple access
  • resource allocation

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