Bayesian Bilinear Inference for Joint Channel Tracking and Data Detection in Millimeter-Wave MIMO Systems

Takahashi, Takumi; Iimori, Hiroki; Ishibashi, Koji; Ibi, Shinsuke; De Abreu, Giuseppe Thadeu Freitas

doi:https://doi.org/10.1109/TWC.2024.3379122

Bayesian Bilinear Inference for Joint Channel Tracking and Data Detection in Millimeter-Wave MIMO Systems

Takahashi, Takumi; Iimori, Hiroki; Ishibashi, Koji et al.

IEEE Transactions on Wireless Communications, 2024, 23(9), 11136-11153

Number of Access:415（2025-08-14 18:26 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/98348

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Title	Bayesian Bilinear Inference for Joint Channel Tracking and Data Detection in Millimeter-Wave MIMO Systems
Creator	NRID 1000040844204 1000040844204 researchmap permalink takumi_takahashi takumi_takahashi Scopus Author ID 57193615052 57193615052 CreatorName Takahashi, Takumi
	CreatorName Iimori, Hiroki
	NRID 1000080452176 1000080452176 CreatorName Ishibashi, Koji
	NRID 1000010448087 1000010448087 CreatorName Ibi, Shinsuke
	CreatorName De Abreu, Giuseppe Thadeu Freitas
Subject	Bayesian bilinear inference
	channel aging
	channel tracking
	data detection
	mmWave communications
Description	Takahashi T., Iimori H., Ishibashi K., et al. Bayesian Bilinear Inference for Joint Channel Tracking and Data Detection in Millimeter-Wave MIMO Systems. IEEE Transactions on Wireless Communications 23, 11136 (2024); https://doi.org/10.1109/TWC.2024.3379122.
Abstract	We propose a novel joint channel tracking and data detection (JCTDD) scheme to combat the channel aging phenomenon typical of millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication systems in high-mobility scenarios. The contribution aims to significantly reduce the communication overhead required to estimate time-varying mmWave channels by leveraging a Bayesian message passing framework based on Gaussian approximation, to jointly perform channel tracking (CT) and data detection (DD). The proposed method can be interpreted as an extension of the Kalman filter-based two-stage tracking mechanism to a Bayesian bilinear inference (BBI)-based joint channel and data estimation (JCDE) framework, featuring the ability to predict future channel state information (CSI) from both reference and payload signals by using an auto-regressive (AR) model describing the time variability of mmWave channel as a state transition model in a bilinear inference algorithm. The resulting JCTDD scheme allows us to track the symbol-by-symbol time variation of channels without embedding additional pilots, leaving any added redundancy to be exploited for channel coding, dramatically improving system performance. The efficacy of the proposed method is confirmed by computer simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also approaches the performance of an idealized Genie-aided scheme.
Publisher	Institute of Electrical and Electronics Engineers Inc.
Source Title	IEEE Transactions on Wireless Communications
Volume (Issue)	23(9)
Page	11136-11153
Date of Issued	2024-09-01
Language	English
Handle URL	https://hdl.handle.net/11094/98348
PISSN	15361276
eISSN	15582248
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1109/TWC.2024.3379122
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCTERMS.bibliographicCitation	IEEE Transactions on Wireless Communications. 2024, 23(9), p. 11136-11153
DC.title	Bayesian Bilinear Inference for Joint Channel Tracking and Data Detection in Millimeter-Wave MIMO Systems
DC.creator	Takahashi, Takumi
	Iimori, Hiroki
	Ishibashi, Koji
	Ibi, Shinsuke
	De Abreu, Giuseppe Thadeu Freitas
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	English
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-09-01
DC.identifier	https://doi.org/10.1109/TWC.2024.3379122
DC.subject	Bayesian bilinear inference
	channel aging
	channel tracking
	data detection
	mmWave communications
DC.description	Takahashi T., Iimori H., Ishibashi K., et al. Bayesian Bilinear Inference for Joint Channel Tracking and Data Detection in Millimeter-Wave MIMO Systems. IEEE Transactions on Wireless Communications 23, 11136 (2024); https://doi.org/10.1109/TWC.2024.3379122.
DCTERMS.abstract	We propose a novel joint channel tracking and data detection (JCTDD) scheme to combat the channel aging phenomenon typical of millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication systems in high-mobility scenarios. The contribution aims to significantly reduce the communication overhead required to estimate time-varying mmWave channels by leveraging a Bayesian message passing framework based on Gaussian approximation, to jointly perform channel tracking (CT) and data detection (DD). The proposed method can be interpreted as an extension of the Kalman filter-based two-stage tracking mechanism to a Bayesian bilinear inference (BBI)-based joint channel and data estimation (JCDE) framework, featuring the ability to predict future channel state information (CSI) from both reference and payload signals by using an auto-regressive (AR) model describing the time variability of mmWave channel as a state transition model in a bilinear inference algorithm. The resulting JCTDD scheme allows us to track the symbol-by-symbol time variation of channels without embedding additional pilots, leaving any added redundancy to be exploited for channel coding, dramatically improving system performance. The efficacy of the proposed method is confirmed by computer simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also approaches the performance of an idealized Genie-aided scheme.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	98348
citation_title	Bayesian Bilinear Inference for Joint Channel Tracking and Data Detection in Millimeter-Wave MIMO Systems
citation_author	Takahashi, Takumi
	Iimori, Hiroki
	Ishibashi, Koji
	Ibi, Shinsuke
	De Abreu, Giuseppe Thadeu Freitas
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	English
citation_date	2024-09-01
citation_journal_title	IEEE Transactions on Wireless Communications
citation_volume	23
citation_issue	9
citation_firstpage	11136
citation_lastpage	11153
citation_doi	https://doi.org/10.1109/TWC.2024.3379122
citation_public_url	https://hdl.handle.net/11094/98348

Bayesian Bilinear Inference for Joint Channel Tracking and Data Detection in Millimeter-Wave MIMO Systems

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