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:329（2025-07-27 00:11 Counts）

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

Link to primary information 

File Format Terms of use Size Views Date.Available Description information
IEEEInternetThingsJ_JIOT_2024.3409698 pdf None 1.60 MB 36 2024.08.01  
Creative Commons : Attribution - NonCommercial - NoDerivatives

Item Information

Output File Export EndNote Basic Export Mendeley

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