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| Presentation | 2023-01-25 11:30 Channel and Data Estimation via Deep Unfolding-Aided BiGaBP for Correlated Large MIMO Koichi Maki, Tetsushi Ikegami (Meiji Univ.)  IT2022-54 SIP2022-105 RCS2022-233 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The study aims to suppress the performance degradation in a correlated fading environment by applying deep unfolding (DU) to BiGaBP (Bilinear Gaussian Belief Propagation), which is known as a JCDE (Joint Channel and Data Estimation) method. The attempts to solve parametric bilinear inference such as JCDE in a Bayesian inferential manner based on belief propagation have been achieved with sufficiently low computational complexity using PBiGAMP (Parametric Bilinear Generalized Approximate Message Passing) and BiGaBP. In particular, BiGaBP has been confirmed to be effective because its dependence on the large-system limit approximation is lower than that of PBiGAMP. However, both methods are derived based on the assumption of large-scale observations following an independent homogeneous distribution with mean zero, so performance degradation under fading spatial correlation cannot be avoided. In this paper, we set the three scaling and damping parameters, which have been artificially designed in conventional methods, as trainable parameters, and performed data-driven tuning to improve the error floor in a correlated environment. In addition, computer simulations based on the iterative error back propagation algorithm theoretically derived from the signal flow graph were performed to demonstrate the effectiveness of the proposal in terms of bit error rate characteristics and to improve the transparency of the DU. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Correlated large MIMO detection / BiGaBP / JCDE / deep unfolding / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 357, RCS2022-233, pp. 142-147, Jan. 2023. | |
| Paper # | RCS2022-233 | |
| Date of Issue | 2023-01-17 (IT, SIP, 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 | IT2022-54 SIP2022-105 RCS2022-233 |
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| Conference Information | |
| Committee | IT RCS SIP |
| Conference Date | 2023-01-24 - 2023-01-25 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Maebashi Terrsa |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2023-01-IT-RCS-SIP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Channel and Data Estimation via Deep Unfolding-Aided BiGaBP for Correlated Large MIMO |
| Sub Title (in English) | |
| Keyword(1) | Correlated large MIMO detection |
| Keyword(2) | BiGaBP |
| Keyword(3) | JCDE |
| Keyword(4) | deep unfolding |
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| 1st Author's Name | Koichi Maki |
| 1st Author's Affiliation | Meiji University (Meiji Univ.) |
| 2nd Author's Name | Tetsushi Ikegami |
| 2nd Author's Affiliation | Meiji University (Meiji Univ.) |
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| Speaker | Author-1 |
| Date Time | 2023-01-25 11:30:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | IT2022-54, SIP2022-105, RCS2022-233 |
| Volume (vol) | vol.122 |
| Number (no) | no.355(IT), no.356(SIP), no.357(RCS) |
| Page | pp.142-147 |
| #Pages | 6 |
| Date of Issue | 2023-01-17 (IT, SIP, RCS) |
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