Deep Learning Based Hybrid Multiple Access Consisting of SCMA and OFDMA Using User Position Information

Yuta Kumagai, Naoya Gonda, Yukiko Shimbo, Hirofumi Suganuma, Fumiaki Maehara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a deep-learning-based uplink hybrid multiple access scheme consisting of both sparse code multiple access (SCMA) and orthogonal frequency-division multiple access (OFDMA). SCMA improves the system throughput when the carrier-To-noise ratio (CNR) is high. However, SCMA performance is significantly degraded, compared to OFDMA, when the CNR is low. To overcome this problem, the proposed scheme introduces a combination of SCMA and OFDMA as a novel multiple access pattern. The scheme determines the appropriate pattern among SCMA-only, OFDMA-only, or their combination, by utilizing user position information through deep learning. The effectiveness of the proposed scheme is demonstrated in terms of system throughput under different user distributions via computer simulations.

Original languageEnglish
Title of host publication3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages10-13
Number of pages4
ISBN (Electronic)9781728176383
DOIs
Publication statusPublished - 2021 Apr 13
Event3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 - Jeju Island, Korea, Republic of
Duration: 2021 Apr 132021 Apr 16

Publication series

Name3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021

Conference

Conference3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021
CountryKorea, Republic of
CityJeju Island
Period21/4/1321/4/16

Keywords

  • Sparse code multiple access (SCMA)
  • deep learning
  • orthogonal frequency-division multiple access (OFDMA)
  • system throughput
  • user position information

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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