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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ10-13
ページ数4
ISBN(電子版)9781728176383
DOI
出版ステータスPublished - 2021 4 13
イベント3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 - Jeju Island, Korea, Republic of
継続期間: 2021 4 132021 4 16

出版物シリーズ

名前3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021

Conference

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

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • 情報システム
  • 情報システムおよび情報管理

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