Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11229
Title: User selection for NOMA based MIMO with physical layer network coding in internet of things applications
Authors: Yılmaz, Saadet Simay
Özbek, Berna
İlgüy, Mert
Okyere, Bismark
Musavian, Leila
Gonzalez, Jonathan
Keywords: IoT devices
MIMO
NOMA
PNC
User-Set Selection
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Non-orthogonal multiple access (NOMA) based multiple-input multiple-output (MIMO), which has the potential to provide both massive connectivity and high spectrum efficiency, is considered as one of the efficient techniques for sixth generation (6G) wireless systems. In massive Internet of Things (IoT) networks, user-set selection is crucial for enhancing the overall performance of NOMA based systems when compared with orthogonal multiple access (OMA) techniques. In this paper, we propose a user-set selection algorithm for IoT uplink transmission to improve the sum data rate of the NOMA based MIMO systems. In order to exchange data between the selected IoT pairs, we propose to employ wireless physical layer network coding (PNC) to further improve the spectral efficiency and reduce the delay to fulfill the requirements of future IoT applications. Performance evaluations are provided based on both sum data rate and bit error rate for the proposed NOMA based MIMO with PNC in the considered massive IoT scenarios. IEEE
URI: http://doi.org/10.1109/JIOT.2021.3079157
https://hdl.handle.net/11147/11229
ISSN: 2327-4662
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File SizeFormat 
User_Selection.pdf926.95 kBAdobe PDFView/Open
Show full item record



CORE Recommender

SCOPUSTM   
Citations

10
checked on Nov 22, 2024

WEB OF SCIENCETM
Citations

7
checked on Nov 23, 2024

Page view(s)

334
checked on Nov 18, 2024

Download(s)

282
checked on Nov 18, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.