Learning Stabilization in Deep Unfolding of Generalized Approximate Message Passing

Furudoi, Tomoharu; Takahashi, Takumi; Ibi, Shinsuke; Ochiai, Hideki

doi:https://doi.org/10.1109/WCNC61545.2025.10978405

Learning Stabilization in Deep Unfolding of Generalized Approximate Message Passing

Furudoi, Tomoharu; Takahashi, Takumi; Ibi, Shinsuke et al.

IEEE Wireless Communications and Networking Conference, WCNC, 2025

アクセス数:33件（2025-09-28 10:26 集計）

固定URL: https://hdl.handle.net/11094/102538

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タイトル	Learning Stabilization in Deep Unfolding of Generalized Approximate Message Passing
著者	著者名 Furudoi, Tomoharu
	NRID 1000040844204 1000040844204 researchmap permalink takumi_takahashi takumi_takahashi Scopus Author ID 57193615052 57193615052 著者名 Takahashi, Takumi
	NRID 1000010448087 1000010448087 著者名 Ibi, Shinsuke
	NRID 1000020334576 1000020334576 researchmap permalink hideki_ochiai hideki_ochiai 著者名 Ochiai, Hideki
キーワード等	data-driven tuning
	deep unfolding
	Generalized approximate message passing
	MIMO signal detection
内容	Furudoi T., Takahashi T., Ibi S., et al. Learning Stabilization in Deep Unfolding of Generalized Approximate Message Passing. IEEE Wireless Communications and Networking Conference, WCNC , (2025); https://doi.org/10.1109/WCNC61545.2025.10978405.
抄録	Generalized approximate message passing (GAMP) achieves near-optimal performance in detecting spatially multiplexed massive multiple-input multiple-output (MIMO) signals with significantly reduced computational complexity under independent and identically distributed (i.i.d.) measurements. However, in spatially correlated MIMO channels where the ideal assumption of large-scale uncorrelated observation does not hold, the detection capability severely deteriorates. This performance degradation can be compensated for by optimizing GAMP with embedded learnable parameters via deep unfolding (DU) techniques, i.e., data-driven tuning; however, the learning process becomes quite unstable. To address this issue, we propose a novel method that achieves stable learning by incorporating a monotonic increase constraint on the reliability of propagated messages by learning the differential (incremental) values of the learnable parameters between two consecutive iterations. The efficacy of the proposed method is confirmed through numerical results in terms of loss trajectory in the learning process and bit error rate (BER) of massive MIMO detection.
出版者	Institute of Electrical and Electronics Engineers Inc.
収録物名	IEEE Wireless Communications and Networking Conference, WCNC
刊行年月	2025-05-09
言語	英語
ハンドルURL	https://hdl.handle.net/11094/102538
PISSN	15253511
eISSN	15582612
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.1109/WCNC61545.2025.10978405
アクセス権	open access
権利情報	© 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.
出版タイプ	Accepted Manuscript
カテゴリ	会議発表論文 Conference Paper

資源タイプ	会議発表論文
ローカル資源タイプ	会議発表論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	IEEE Wireless Communications and Networking Conference, WCNC. 2025
DC.title	Learning Stabilization in Deep Unfolding of Generalized Approximate Message Passing
DC.creator	Furudoi, Tomoharu
	Takahashi, Takumi
	Ibi, Shinsuke
	Ochiai, Hideki
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-05-09
DC.identifier	https://doi.org/10.1109/WCNC61545.2025.10978405
DC.subject	data-driven tuning
	deep unfolding
	Generalized approximate message passing
	MIMO signal detection
DC.description	Furudoi T., Takahashi T., Ibi S., et al. Learning Stabilization in Deep Unfolding of Generalized Approximate Message Passing. IEEE Wireless Communications and Networking Conference, WCNC , (2025); https://doi.org/10.1109/WCNC61545.2025.10978405.
DCTERMS.abstract	Generalized approximate message passing (GAMP) achieves near-optimal performance in detecting spatially multiplexed massive multiple-input multiple-output (MIMO) signals with significantly reduced computational complexity under independent and identically distributed (i.i.d.) measurements. However, in spatially correlated MIMO channels where the ideal assumption of large-scale uncorrelated observation does not hold, the detection capability severely deteriorates. This performance degradation can be compensated for by optimizing GAMP with embedded learnable parameters via deep unfolding (DU) techniques, i.e., data-driven tuning; however, the learning process becomes quite unstable. To address this issue, we propose a novel method that achieves stable learning by incorporating a monotonic increase constraint on the reliability of propagated messages by learning the differential (incremental) values of the learnable parameters between two consecutive iterations. The efficacy of the proposed method is confirmed through numerical results in terms of loss trajectory in the learning process and bit error rate (BER) of massive MIMO detection.
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	102538
citation_title	Learning Stabilization in Deep Unfolding of Generalized Approximate Message Passing
citation_author	Furudoi, Tomoharu
	Takahashi, Takumi
	Ibi, Shinsuke
	Ochiai, Hideki
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	英語
citation_date	2025-05-09
citation_journal_title	IEEE Wireless Communications and Networking Conference, WCNC
citation_doi	https://doi.org/10.1109/WCNC61545.2025.10978405
citation_public_url	https://hdl.handle.net/11094/102538

Learning Stabilization in Deep Unfolding of Generalized Approximate Message Passing

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