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Paper Abstract and Keywords
Presentation 2024-06-20 16:30
A Study on Sparse Bayesian Learning Using Complex t-Prior for Beam-domain Channel Estimation in Massive Multi-User MIMO
Kengo Furuta, Takumi Takahashi, Hideki Ochiai (Osaka Univ.) RCS2024-67
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a novel beam-domain channel estimation (CE) algorithm based on sparse Bayesian learning (SBL) using complex t-prior for massive multi-user multi-input multi-output (MU-MIMO) systems.In the presence of digital beamforming at the receiver, the equivalent channel in the beam domain is characterized not by a sparse structure consisting of zero and nonzero elements, but by a structure consisting of many small nonzero elements and a few large elements, due to insufficient observation resolution and the sidelobe leakage caused by the physical constraints of MU-MIMO systems.To fully capture this pseudo-sparse structure, we incorporate a complex t}-distribution with appropriate degrees of freedom (DoF) into the SBL algorithm via a hierarchical Bayesian model.This heavy-tailed prior allows for efficient beam-domain CE accounting for small but non-negligible elements, which is verified by the analysis of regularization based on an equivalent optimization problem.Computer simulations in massive MU-MIMO systems operating under sub-6 GHz bands show that the proposed method significantly outperforms conventional sparse signal recovery (SSR)-based methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Massive MIMO / channel estimation / sparse Bayesian learning / hierarchical Bayesian model / complex t-distribution / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 84, RCS2024-67, pp. 235-240, June 2024.
Paper # RCS2024-67 
Date of Issue 2024-06-12 (RCS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF RCS2024-67

Conference Information
Committee RCS  
Conference Date 2024-06-19 - 2024-06-21 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) First Presentation in IEICE Technical Committee, Resource Control, Scheduling, Wireless Communications, etc. 
Paper Information
Registration To RCS 
Conference Code 2024-06-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Sparse Bayesian Learning Using Complex t-Prior for Beam-domain Channel Estimation in Massive Multi-User MIMO 
Sub Title (in English)  
Keyword(1) Massive MIMO  
Keyword(2) channel estimation  
Keyword(3) sparse Bayesian learning  
Keyword(4) hierarchical Bayesian model  
Keyword(5) complex t-distribution  
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1st Author's Name Kengo Furuta  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Takumi Takahashi  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Hideki Ochiai  
3rd Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2024-06-20 16:30:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2024-67 
Volume (vol) vol.124 
Number (no) no.84 
Page pp.235-240 
#Pages 6 
Date of Issue 2024-06-12 (RCS) 

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