Detection for 5G-NOMA: An online adaptive machine learning approach

Daniyal Amir Awan, Renato L.G. Cavalcante, Masahiro Yukawa, Slawomir Stanczak

研究成果: Conference contribution

21 被引用数 (Scopus)

抄録

Non-orthogonal multiple access (NOMA) has emerged as a promising radio access technique for enabling the performance enhancements promised by the fifth-generation (5G) networks in terms of connectivity, latency, and spectrum efficiency. In the NOMA uplink, detection based on successive interference cancellation (SIC) with device clustering has been suggested. If the receivers are equipped with multiple antennas, SIC can be combined with minimum mean-squared error (MMSE) beamforming. However, there exists a tradeoff between the NOMA cluster size and the incurred SIC error. Larger clusters lead to larger errors but they are desirable from the spectrum efficiency and connectivity point of view. To enable the deployment of large clusters, we propose a novel online learning detection method for the NOMA uplink. We design an online adaptive filter in the sum space of linear and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design is robust against variations of a dynamic wireless network that can deteriorate the performance of a purely nonlinear adaptive filter. We demonstrate by simulations that the proposed method outperforms (symbol level) MMSE-SIC based detection for large cluster sizes.

本文言語English
ホスト出版物のタイトル2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(印刷版)9781538631805
DOI
出版ステータスPublished - 2018 7月 27
イベント2018 IEEE International Conference on Communications, ICC 2018 - Kansas City, United States
継続期間: 2018 5月 202018 5月 24

出版物シリーズ

名前IEEE International Conference on Communications
2018-May
ISSN(印刷版)1550-3607

Other

Other2018 IEEE International Conference on Communications, ICC 2018
国/地域United States
CityKansas City
Period18/5/2018/5/24

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学

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