Stochastic Geometry-Based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA

Hirai, Takeshi; Ueda, Yuta; Wakamiya, Naoki

doi:https://doi.org/10.1109/JIOT.2024.3409698

Stochastic Geometry-Based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA

Hirai, Takeshi; Ueda, Yuta; Wakamiya, Naoki

IEEE Internet of Things Journal, 2024, 11(18), 29926-29940

Number of Access:322（2025-07-12 21:49 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/97267

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Title	Stochastic Geometry-Based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA
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 Ueda, Yuta
	NRID 1000050283742 1000050283742 researchmap permalink wakamiya wakamiya Scopus Author ID 7007045118 7007045118 CreatorName Wakamiya, Naoki
Subject	Analytical models
	Fading channels
	grant-free NOMA
	Internet of Things
	mMTC
	NOMA
	Nonhomogeneous media
	power collisions
	SIC
	stochastic geometry
	Stochastic processes
	Throughput
	throughput analysis
Description	Hirai T., Ueda Y., Wakamiya N.. Stochastic Geometry-Based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA. IEEE Internet of Things Journal, (2024); https://doi.org/10.1109/JIOT.2024.3409698.
Abstract	This paper proposes a stochastic geometry-based analytical framework for the throughput of the grant-free power-domain non-orthogonal multiple access (GF-NOMA) with user-specific constraints of selectable power levels and analyzes the achievable throughput. Our analytical framework uses stochastic geometry to reflect selectable power levels constrained by the maximum transmission power and channel of each user to an inhomogeneous offered load per level. This key idea enables our framework to analyze the throughput bounded by the geographical user distribution and derive a suitable selection strategy of power levels under the constraint more accurately than the existing models. Our analytical results showed that our framework analyzed the throughput with only an analysis error of 0.1% compared with the Monte Carlo simulations, although the existing model overestimated 58% higher throughput. By using the proposed analytical model, our results presented decreasing the achievable throughput with increasing the coverage range. This paper also proposes a heuristic method based on our proposed analytical model to derive a suitable selection strategy of power levels. Our results highlight that the derived selection strategy on our analytical framework achieved 20 higher throughput than the baseline strategy, where each user randomly selects a power level under the power level constraint.
Publisher	Institute of Electrical and Electronics Engineers Inc.
Source Title	IEEE Internet of Things Journal
Volume (Issue)	11(18)
Page	29926-29940
Date of Issued	2024-06-05
Language	English
Handle URL	https://hdl.handle.net/11094/97267
eISSN	23274662
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1109/JIOT.2024.3409698
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

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Resource Type	journal article
Local Type	Journal Article
DCTERMS.bibliographicCitation	IEEE Internet of Things Journal. 2024, 11(18), p. 29926-29940
DC.title	Stochastic Geometry-Based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA
DC.creator	Hirai, Takeshi
	Ueda, Yuta
	Wakamiya, Naoki
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	English
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-06-05
DC.identifier	https://doi.org/10.1109/JIOT.2024.3409698
DC.subject	Analytical models
	Fading channels
	grant-free NOMA
	Internet of Things
	mMTC
	NOMA
	Nonhomogeneous media
	power collisions
	SIC
	stochastic geometry
	Stochastic processes
	Throughput
	throughput analysis
DC.description	Hirai T., Ueda Y., Wakamiya N.. Stochastic Geometry-Based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA. IEEE Internet of Things Journal, (2024); https://doi.org/10.1109/JIOT.2024.3409698.
DCTERMS.abstract	This paper proposes a stochastic geometry-based analytical framework for the throughput of the grant-free power-domain non-orthogonal multiple access (GF-NOMA) with user-specific constraints of selectable power levels and analyzes the achievable throughput. Our analytical framework uses stochastic geometry to reflect selectable power levels constrained by the maximum transmission power and channel of each user to an inhomogeneous offered load per level. This key idea enables our framework to analyze the throughput bounded by the geographical user distribution and derive a suitable selection strategy of power levels under the constraint more accurately than the existing models. Our analytical results showed that our framework analyzed the throughput with only an analysis error of 0.1% compared with the Monte Carlo simulations, although the existing model overestimated 58% higher throughput. By using the proposed analytical model, our results presented decreasing the achievable throughput with increasing the coverage range. This paper also proposes a heuristic method based on our proposed analytical model to derive a suitable selection strategy of power levels. Our results highlight that the derived selection strategy on our analytical framework achieved 20 higher throughput than the baseline strategy, where each user randomly selects a power level under the power level constraint.
DC.rights	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
DC.format	97267
citation_title	Stochastic Geometry-Based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA
citation_author	Hirai, Takeshi
	Ueda, Yuta
	Wakamiya, Naoki
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	English
citation_date	2024-06-05
citation_journal_title	IEEE Internet of Things Journal
citation_volume	11
citation_issue	18
citation_firstpage	29926
citation_lastpage	29940
citation_doi	https://doi.org/10.1109/JIOT.2024.3409698
citation_public_url	https://hdl.handle.net/11094/97267