Bilinear Gaussian Belief Propagation for Massive MIMO Detection with Non-Orthogonal Pilots

Ito, Kenta; Takahashi, Takumi; Ibi, Shinsuke; Sampei, Seiichi

doi:https://doi.org/10.1109/TCOMM.2023.3325479

Bilinear Gaussian Belief Propagation for Massive MIMO Detection with Non-Orthogonal Pilots

Ito, Kenta; Takahashi, Takumi; Ibi, Shinsuke et al.

IEEE Transactions on Communications, 2023, 72(2), 1045-1061

Number of Access:497（2025-08-16 02:40 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/93323

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Title	Bilinear Gaussian Belief Propagation for Massive MIMO Detection with Non-Orthogonal Pilots
Creator	CreatorName Ito, Kenta
	NRID 1000040844204 1000040844204 researchmap permalink takumi_takahashi takumi_takahashi Scopus Author ID 57193615052 57193615052 CreatorName Takahashi, Takumi
	NRID 1000010448087 1000010448087 CreatorName Ibi, Shinsuke
	NRID 1000050252599 1000050252599 researchmap permalink seiichisampei seiichisampei Scopus Author ID 7004748293 7004748293 CreatorName Sampei, Seiichi
Subject	Approximation algorithms
	Bayesian bilinear inference
	Behavioral sciences
	belief propagation
	Belief propagation
	belief scaling
	BiGAMP
	Channel estimation
	Estimation
	Massive multi-user MIMO
	Multiuser detection
	Symbols
Description	Ito K., Takahashi T., Ibi S., et al. Bilinear Gaussian Belief Propagation for Massive MIMO Detection with Non-Orthogonal Pilots. IEEE Transactions on Communications , (2023); https://doi.org/10.1109/TCOMM.2023.3325479.
Abstract	We propose a novel joint channel and data estimation (JCDE) algorithm via bilinear Gaussian belief propagation (BiGaBP) for massive multi-user MIMO (MU-MIMO) systems with non-orthogonal pilot sequences. The contribution aims to reduce significantly the communication overhead required for channel acquisition by enabling the use of short non-orthogonal pilots, while maintaining multi-user detection (MUD) capability. Bilinear generalized approximate message passing (BiGAMP), which is systematically derived by extending approximate message passing (AMP) to the bilinear inference problem (BIP), provides computationally efficient approximate implementations of large-scale JCDE via sum-product algorithm (SPA); however, as the pilot length decreases, the estimation accuracy is severely degraded. To tackle this issue, the proposed BiGaBP algorithm generalizes BiGAMP by relaxing its dependence on the large-system limit approximation and leveraging the belief propagation (BP) concept. In addition, a novel belief scaling method complying with the data detection accuracy for each iteration step is designed to avoid the divergence behavior of iterative estimation in the early iterations due to the use of non-orthogonal pilots, especially in insufficient large-system conditions. Simulation results show that the proposed method outperforms the state-of-the-art schemes and approaches the performance of idealized (genie-aided) scheme in terms of mean square error (MSE) and bit error rate (BER) performances.
Publisher	Institute of Electrical and Electronics Engineers Inc.
Source Title	IEEE Transactions on Communications
Volume (Issue)	72(2)
Page	1045-1061
Date of Issued	2023-10-18
Language	English
Handle URL	https://hdl.handle.net/11094/93323
PISSN	00906778
eISSN	15580857
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1109/TCOMM.2023.3325479
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version	Proof
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	Text
DCTERMS.bibliographicCitation	IEEE Transactions on Communications. 2023, 72(2), p. 1045-1061
DC.title	Bilinear Gaussian Belief Propagation for Massive MIMO Detection with Non-Orthogonal Pilots
DC.creator	Ito, Kenta
	Takahashi, Takumi
	Ibi, Shinsuke
	Sampei, Seiichi
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	2023-10-18
DC.identifier	https://doi.org/10.1109/TCOMM.2023.3325479
DC.subject	Approximation algorithms
	Bayesian bilinear inference
	Behavioral sciences
	belief propagation
	Belief propagation
	belief scaling
	BiGAMP
	Channel estimation
	Estimation
	Massive multi-user MIMO
	Multiuser detection
	Symbols
DC.description	Ito K., Takahashi T., Ibi S., et al. Bilinear Gaussian Belief Propagation for Massive MIMO Detection with Non-Orthogonal Pilots. IEEE Transactions on Communications , (2023); https://doi.org/10.1109/TCOMM.2023.3325479.
DCTERMS.abstract	We propose a novel joint channel and data estimation (JCDE) algorithm via bilinear Gaussian belief propagation (BiGaBP) for massive multi-user MIMO (MU-MIMO) systems with non-orthogonal pilot sequences. The contribution aims to reduce significantly the communication overhead required for channel acquisition by enabling the use of short non-orthogonal pilots, while maintaining multi-user detection (MUD) capability. Bilinear generalized approximate message passing (BiGAMP), which is systematically derived by extending approximate message passing (AMP) to the bilinear inference problem (BIP), provides computationally efficient approximate implementations of large-scale JCDE via sum-product algorithm (SPA); however, as the pilot length decreases, the estimation accuracy is severely degraded. To tackle this issue, the proposed BiGaBP algorithm generalizes BiGAMP by relaxing its dependence on the large-system limit approximation and leveraging the belief propagation (BP) concept. In addition, a novel belief scaling method complying with the data detection accuracy for each iteration step is designed to avoid the divergence behavior of iterative estimation in the early iterations due to the use of non-orthogonal pilots, especially in insufficient large-system conditions. Simulation results show that the proposed method outperforms the state-of-the-art schemes and approaches the performance of idealized (genie-aided) scheme in terms of mean square error (MSE) and bit error rate (BER) performances.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	93323
citation_title	Bilinear Gaussian Belief Propagation for Massive MIMO Detection with Non-Orthogonal Pilots
citation_author	Ito, Kenta
	Takahashi, Takumi
	Ibi, Shinsuke
	Sampei, Seiichi
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	English
citation_date	2023-10-18
citation_journal_title	IEEE Transactions on Communications
citation_volume	72
citation_issue	2
citation_firstpage	1045
citation_lastpage	1061
citation_doi	https://doi.org/10.1109/TCOMM.2023.3325479
citation_public_url	https://hdl.handle.net/11094/93323

Bilinear Gaussian Belief Propagation for Massive MIMO Detection with Non-Orthogonal Pilots

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