Power-Level-Design-Aware Scalable Framework for Throughput Analysis of GF-NOMA in mMTC

Hirai, Takeshi; Oda, Rei; Wakamiya, Naoki

IEEE Internet of Things Journal, 2024, 11(17), 28227-28243

Number of Access:305（2025-07-21 22:14 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/97223

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Title
Power-Level-Design-Aware Scalable Framework for Throughput Analysis of GF-NOMA in mMTC
Creator
NRID
1000030903554 1000030903554
ORCID
0000-0002-1090-4770 0000-0002-1090-4770
researchmap permalink
takeshihirai takeshihirai
Scopus Author ID
57203993597 57203993597
CreatorName
Hirai, Takeshi
CreatorName
Oda, Rei
NRID
1000050283742 1000050283742
researchmap permalink
wakamiya wakamiya
Scopus Author ID
7007045118 7007045118
CreatorName
Wakamiya, Naoki
Subject
mMTC
grant-free
power-domain
NOMA
SIC
channel inversion
power collision
per-level offered load
Description
Hirai T., Oda R., Wakamiya N.. Power-Level-Design-Aware Scalable Framework for Throughput Analysis of GF-NOMA in mMTC. IEEE Internet of Things Journal , (2024); https://doi.org/10.1109/JIOT.2024.3400996.
Abstract
This paper proposes a scalable framework to analyze the throughput of the grant-free power-domain nonorthogonal multiple access (GF-NOMA) and presents the achievable performance in the optimized offered load at each power level (called per-level offered load) by using our framework. Our analytical model reflects packet errors caused by power collisions, characterized by GF-NOMA, based on the power level design guaranteeing the required signal-to-interference-and-noise ratio (SINR). This key idea enables analyzing the throughput of a large-scale GF-NOMA system more accurately than the existing analytical models. Also, this key idea enables optimizing the per-level offered load rather than a uniform one in typical optimization problems related to the throughput: the throughput maximization or energy minimization problem with a throughput condition. Our analytical results highlight some key insights into designing future access control methods in GF-NOMA. First, our analytical model achieves an approximation error of only 0.4% for the exact throughput obtained by the exhaustive search at five power levels; the existing analytical model provides an approximation error of 25%. Next, our proposed framework highlights that the optimal per-level offered load restrictively improves the throughput above the optimally uniform per-level offered load. Finally, our proposed framework discovers a 27 more energy-efficient per-level offered load than the existing framework at five power levels while providing higher throughput than the optimally uniform per-level offered load.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Source Title
IEEE Internet of Things Journal
Volume (Issue)
11(17)
Page
28227-28243
Date of Issued
2024-05-14
Language
English
Handle URL
https://hdl.handle.net/11094/97223
eISSN
23274662
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1109/JIOT.2024.3400996
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article