Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation

Furuta, Kengo; Takahashi, Takumi; Ochiai, Hideki

doi:https://doi.org/10.1109/OJCOMS.2024.3457507

Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation

Furuta, Kengo; Takahashi, Takumi; Ochiai, Hideki

IEEE Open Journal of the Communications Society, 2024, 5, 5905-5920

アクセス数:397件（2025-09-30 08:11 集計）

固定URL: https://hdl.handle.net/11094/98347

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タイトル	Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation
著者	著者名 Furuta, Kengo
	NRID 1000040844204 1000040844204 researchmap permalink takumi_takahashi takumi_takahashi Scopus Author ID 57193615052 57193615052 著者名 Takahashi, Takumi
	NRID 1000020334576 1000020334576 researchmap permalink hideki_ochiai hideki_ochiai 著者名 Ochiai, Hideki
キーワード等	beam-domain signal processing
	channel estimation
	complex t-distribution
	hierarchical Bayesian model
	Massive MIMO
	sparse Bayesian learning
内容	Furuta K., Takahashi T., Ochiai H. Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation. IEEE Open Journal of the Communications Society 5, 5905 (2024); https://doi.org/10.1109/OJCOMS.2024.3457507.
抄録	This paper proposes a novel beam-domain channel estimation (CE) algorithm via sparse Bayesian learning (SBL) using complex t-prior for massive multi-user multiple-input multiple-output (MIMO) systems. Due to the sidelobe leakage and insufficient observation resolution resulting from physical constraints, the equivalent channel after digital beamforming at the receiver has a structure with many small but non-zero elements, which cannot be modeled strictly as a sparse signal. To fully capture this pseudo-sparse structure characterized by the signal strength variations among elements, we design a novel SBL algorithm that incorporates a complex t-distribution using a hierarchical Bayesian model. By utilizing a high degree of adaptability of this heavy-tailed prior, it is possible to efficiently learn the signal strength, accounting for elements with non-zero but small values, which is verified by the regularization analysis based on an equivalent optimization problem. The efficacy of the proposed CE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) sparse signal recovery (SSR)-based algorithms but also achieves the performance of a genie-aided scheme over a wide signal-to-noise ratio (SNR) range in both sub-6 GHz and millimeter-wave (mmWave) wireless communication scenarios.
出版者	Institute of Electrical and Electronics Engineers Inc.
収録物名	IEEE Open Journal of the Communications Society
巻(号)	5
ページ	5905-5920
刊行年月	2024-09-10
言語	英語
ハンドルURL	https://hdl.handle.net/11094/98347
eISSN	2644125X
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1109/OJCOMS.2024.3457507
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	IEEE Open Journal of the Communications Society. 2024, 5, p. 5905-5920
DC.title	Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation
DC.creator	Furuta, Kengo
	Takahashi, Takumi
	Ochiai, Hideki
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-09-10
DC.identifier	https://doi.org/10.1109/OJCOMS.2024.3457507
DC.subject	beam-domain signal processing
	channel estimation
	complex t-distribution
	hierarchical Bayesian model
	Massive MIMO
	sparse Bayesian learning
DC.description	Furuta K., Takahashi T., Ochiai H. Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation. IEEE Open Journal of the Communications Society 5, 5905 (2024); https://doi.org/10.1109/OJCOMS.2024.3457507.
DCTERMS.abstract	This paper proposes a novel beam-domain channel estimation (CE) algorithm via sparse Bayesian learning (SBL) using complex t-prior for massive multi-user multiple-input multiple-output (MIMO) systems. Due to the sidelobe leakage and insufficient observation resolution resulting from physical constraints, the equivalent channel after digital beamforming at the receiver has a structure with many small but non-zero elements, which cannot be modeled strictly as a sparse signal. To fully capture this pseudo-sparse structure characterized by the signal strength variations among elements, we design a novel SBL algorithm that incorporates a complex t-distribution using a hierarchical Bayesian model. By utilizing a high degree of adaptability of this heavy-tailed prior, it is possible to efficiently learn the signal strength, accounting for elements with non-zero but small values, which is verified by the regularization analysis based on an equivalent optimization problem. The efficacy of the proposed CE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) sparse signal recovery (SSR)-based algorithms but also achieves the performance of a genie-aided scheme over a wide signal-to-noise ratio (SNR) range in both sub-6 GHz and millimeter-wave (mmWave) wireless communication scenarios.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	98347
citation_title	Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation
citation_author	Furuta, Kengo
	Takahashi, Takumi
	Ochiai, Hideki
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	英語
citation_date	2024-09-10
citation_journal_title	IEEE Open Journal of the Communications Society
citation_volume	5
citation_firstpage	5905
citation_lastpage	5920
citation_doi	https://doi.org/10.1109/OJCOMS.2024.3457507
citation_public_url	https://hdl.handle.net/11094/98347

Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation

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