Vector Similarity Search-Based MCS Selection for Iterative Signal Detection in Massive Multi-User MIMO-OFDM Systems

Kobayashi, Fuga; Takahashi, Takumi; Ibi, Shinsuke; Ochiai, Hideki; Muraoka, Kazushi; Doi, Takanobu; Ishii, Naoto

doi:https://doi.org/10.1109/WCNC61545.2025.10978596

Vector Similarity Search-Based MCS Selection for Iterative Signal Detection in Massive Multi-User MIMO-OFDM Systems

Kobayashi, Fuga; Takahashi, Takumi; Ibi, Shinsuke et al.

IEEE Wireless Communications and Networking Conference, WCNC, 2025

Number of Access:34（2025-09-30 09:39 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/102537

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Title	Vector Similarity Search-Based MCS Selection for Iterative Signal Detection in Massive Multi-User MIMO-OFDM Systems
Creator	CreatorName Kobayashi, Fuga
	NRID 1000040844204 1000040844204 researchmap permalink takumi_takahashi takumi_takahashi Scopus Author ID 57193615052 57193615052 CreatorName Takahashi, Takumi
	NRID 1000010448087 1000010448087 CreatorName Ibi, Shinsuke
	NRID 1000020334576 1000020334576 researchmap permalink hideki_ochiai hideki_ochiai CreatorName Ochiai, Hideki
	CreatorName Muraoka, Kazushi
	CreatorName Doi, Takanobu
	CreatorName Ishii, Naoto
Subject	MCS selection
	MU-MIMO-OFDM systems
	mutual information
	vector database
	vector similarity search
Description	Kobayashi F., Takahashi T., Ibi S., et al. Vector Similarity Search-Based MCS Selection for Iterative Signal Detection in Massive Multi-User MIMO-OFDM Systems. IEEE Wireless Communications and Networking Conference, WCNC , (2025); https://doi.org/10.1109/WCNC61545.2025.10978596.
Abstract	This paper proposes a novel vector similarity search (VSS)-based modulation and coding scheme (MCS) selection for massive multi-user multiple-input multiple-output orthogonal frequency division multiplexing (MU-MIMO-OFDM) systems that employ uplink multi-user detection (MUD) based on iterative signal estimation. To maximize the uplink throughput of MU-MIMO-OFDM systems, it is necessary to assign a carefully selected MCS to each user according to an accurate prediction of the mutual information (MI) that can be achieved with MUD based on the knowledge of the estimated channel state information (CSI). However, since the detection accuracy of iterative MUDs, such as expectation propagation (EP), varies depending on the convergence characteristics, it is challenging to analytically predict the achievable MI in the presence of MUD. To address this difficulty, we propose a novel method for predicting the achievable MI by creating a vector database (VDB) offline that stores feature vectors (keys) computed from CSI and the actual MI (values) achieved with iterative MUD, and then searching this VDB online using approximate nearest neighbors (ANN) search, which enables VSS at ultra-high speed. Simulation results show that the MCS selection based on the proposed MI prediction achieves higher uplink throughput than the conventional schemes in MU-MIMO-OFDM systems using the EP-based MUD.
Publisher	Institute of Electrical and Electronics Engineers Inc.
Source Title	IEEE Wireless Communications and Networking Conference, WCNC
Date of Issued	2025-05-09
Language	English
Handle URL	https://hdl.handle.net/11094/102537
PISSN	15253511
Relation.isVersionOf	DOI (Publisher Version) https://doi.org/10.1109/WCNC61545.2025.10978596
Access Rights	open access
CopyRight	© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
oaire:version	Accepted Manuscript
Category	会議発表論文 Conference Paper

Resource Type	conference paper
Local Type	Conference Paper
DCMI Type	Text
DCTERMS.bibliographicCitation	IEEE Wireless Communications and Networking Conference, WCNC. 2025
DC.title	Vector Similarity Search-Based MCS Selection for Iterative Signal Detection in Massive Multi-User MIMO-OFDM Systems
DC.creator	Kobayashi, Fuga
	Takahashi, Takumi
	Ibi, Shinsuke
	Ochiai, Hideki
	Muraoka, Kazushi
	Doi, Takanobu
	Ishii, Naoto
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-05-09
DC.identifier	https://doi.org/10.1109/WCNC61545.2025.10978596
DC.subject	MCS selection
	MU-MIMO-OFDM systems
	mutual information
	vector database
	vector similarity search
DC.description	Kobayashi F., Takahashi T., Ibi S., et al. Vector Similarity Search-Based MCS Selection for Iterative Signal Detection in Massive Multi-User MIMO-OFDM Systems. IEEE Wireless Communications and Networking Conference, WCNC , (2025); https://doi.org/10.1109/WCNC61545.2025.10978596.
DCTERMS.abstract	This paper proposes a novel vector similarity search (VSS)-based modulation and coding scheme (MCS) selection for massive multi-user multiple-input multiple-output orthogonal frequency division multiplexing (MU-MIMO-OFDM) systems that employ uplink multi-user detection (MUD) based on iterative signal estimation. To maximize the uplink throughput of MU-MIMO-OFDM systems, it is necessary to assign a carefully selected MCS to each user according to an accurate prediction of the mutual information (MI) that can be achieved with MUD based on the knowledge of the estimated channel state information (CSI). However, since the detection accuracy of iterative MUDs, such as expectation propagation (EP), varies depending on the convergence characteristics, it is challenging to analytically predict the achievable MI in the presence of MUD. To address this difficulty, we propose a novel method for predicting the achievable MI by creating a vector database (VDB) offline that stores feature vectors (keys) computed from CSI and the actual MI (values) achieved with iterative MUD, and then searching this VDB online using approximate nearest neighbors (ANN) search, which enables VSS at ultra-high speed. Simulation results show that the MCS selection based on the proposed MI prediction achieves higher uplink throughput than the conventional schemes in MU-MIMO-OFDM systems using the EP-based MUD.
DC.rights	© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DC.format	102537
citation_title	Vector Similarity Search-Based MCS Selection for Iterative Signal Detection in Massive Multi-User MIMO-OFDM Systems
citation_author	Kobayashi, Fuga
	Takahashi, Takumi
	Ibi, Shinsuke
	Ochiai, Hideki
	Muraoka, Kazushi
	Doi, Takanobu
	Ishii, Naoto
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	English
citation_date	2025-05-09
citation_journal_title	IEEE Wireless Communications and Networking Conference, WCNC
citation_doi	https://doi.org/10.1109/WCNC61545.2025.10978596
citation_public_url	https://hdl.handle.net/11094/102537

Vector Similarity Search-Based MCS Selection for Iterative Signal Detection in Massive Multi-User MIMO-OFDM Systems

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