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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IT | 2023-08-04 09:30 |
Kanagawa | Shonan Institute of Technology (Primary: On-site, Secondary: Online) |
Deep Unfolding-based Distributed MIMO Detection Masaya Kumagai, Ayano Nakai-Kasai, Tadashi Wadayama (NITech)  IT2023-21 |
This paper proposes a distributed multiple-input multiple-output signal detection algorithm based on DU (deep unfolding)... [more] | IT2023-21 pp.38-43 |
| MSS, CAS, SIP, VLD | 2023-07-06 14:40 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Convergence Acceleration of Particle-based Variational Inference by Deep Unfolding Yuya Kawamura, Satoshi Takabe (Tokyo Tech) CAS2023-8 VLD2023-8 SIP2023-24 MSS2023-8 |
Stein Variational Gradient Descent(SVGD) is a prominent particle-based variational inference method used for estimating ... [more] | CAS2023-8 VLD2023-8 SIP2023-24 MSS2023-8 pp.37-42 |
| OCS, OFT, OPE (Joint) [detail] |
2023-02-17 11:05 |
Okinawa | kukuru-itomancity (Primary: On-site, Secondary: Online) |
Designing Operation Parameters Using Deep Unfolding for MIMO Adaptive Equalization in Optical Coherent Transmission Systems Eidai Sunamoto, Koji Igarashi (Osaka Univ.) OCS2022-81 OPE2022-110 |
In the coherent optical transmission systems, adaptive control is required to suppress and compensate for laser phase no... [more] | OCS2022-81 OPE2022-110 pp.56-61(OCS), pp.81-86(OPE) |
| IT, RCS, SIP | 2023-01-24 15:55 |
Gunma | Maebashi Terrsa (Primary: On-site, Secondary: Online) |
Deep Unfolding-based Weighting in Distributed MIMO Detection Masaya Kumagai, Ayano Nakai-Kasai, Tadashi Wadayama (NITech) IT2022-49 SIP2022-100 RCS2022-228 |
This paper proposes distributed uplink MIMO (Multiple-Input Multiple-Output) signal detection algorithms based on DU (d... [more] | IT2022-49 SIP2022-100 RCS2022-228 pp.114-119 |
| IT, RCS, SIP | 2023-01-25 11:30 |
Gunma | Maebashi Terrsa (Primary: On-site, Secondary: Online) |
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 |
The study aims to suppress the performance degradation in a correlated fading environment by applying deep unfolding (DU... [more] | IT2022-54 SIP2022-105 RCS2022-233 pp.142-147 |
| MIKA (3rd) |
2022-10-14 10:40 |
Niigata | Niigata Citizens Plaza (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Channel and Data Estimation via Deep Unfolding-Aided BiGaBP for Correlated Massive MIMO Koichi Maki, Tetsushi Ikegami (Meiji Univ.) |
This paper applies deep unfolding (DU) to a joint channel and data estimation (JCDE) scheme via bilinear belief propagat... [more] | |
| RCS | 2022-06-16 14:55 |
Okinawa | University of the Ryukyus, Senbaru Campus and online (Primary: On-site, Secondary: Online) |
A study on model parameters for MIMO signal detection using learned AMP Mari Miyoshi, Toshihiko Nishimura, Takanori Sato, Takeo Ohgane, Yasutaka Ogawa, Junichiro Hagiwara (Hokkaido Univ.) RCS2022-50 |
Approximate message passing (AMP) is applicable to massive MIMO signal detection and achieves a high detection performan... [more] | RCS2022-50 pp.156-161 |
| RCS | 2022-06-17 10:35 |
Okinawa | University of the Ryukyus, Senbaru Campus and online (Primary: On-site, Secondary: Online) |
A Study on Data-Driven Fine-Tuning for ICA-Aided Blind Signal Separation Taisuke Nogami, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Hisato Iwai (Doshisha Univ.) RCS2022-61 |
Internet of Things (IoT) communications, which will plays an important role in the next-generation wireless communicatio... [more] | RCS2022-61 pp.218-223 |
| IT, ISEC, RCC, WBS | 2022-03-10 14:15 |
Online | Online | Lossy Source Coding of Binary Memoryless Source by Trained LDGM Encoder Kazuki Ogawa, Motohiko Isaka (Kwansei Gakuin Univ.) IT2021-101 ISEC2021-66 WBS2021-69 RCC2021-76 |
Use of low-density generator matrix codes with message-passing algorithm is an efficient approach for lossy coding of th... [more] | IT2021-101 ISEC2021-66 WBS2021-69 RCC2021-76 pp.111-115 |
| IT, ISEC, RCC, WBS | 2022-03-10 14:40 |
Online | Online | Optimum Clustering Method for Data Driven Consensus Problem considering Network Centrality Shoya Ogawa, Koji Ishii (Kagawa Univ) IT2021-109 ISEC2021-74 WBS2021-77 RCC2021-84 |
In consensus problems in complex networks, the convergence performance deeply depends on the weighting factors. IKishida... [more] | IT2021-109 ISEC2021-74 WBS2021-77 RCC2021-84 pp.155-160 |
| IT, ISEC, RCC, WBS | 2022-03-11 12:00 |
Online | Online | A study of distributed MIMO signal detection based on deep unfolding Masaya Kumagai, Ayano Nakai-Kasai, Tadashi Wadayama (NITech) IT2021-113 ISEC2021-78 WBS2021-81 RCC2021-88 |
This paper proposes distributed MIMO (Multiple-Input Multiple-Output) signal detection algorithms based on DU (deep un... [more] |
IT2021-113 ISEC2021-78 WBS2021-81 RCC2021-88 pp.174-179 |
| RCS, SIP, IT | 2022-01-20 14:30 |
Online | Online | A Study on Deep Unfolding-Aided GNSS Positioning Yuki Hayama, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Hisato Iwai (Doshisha Univ.) IT2021-43 SIP2021-51 RCS2021-211 |
In Global Navigation Satellite System (GNSS) positioning, the receiver position is estimated by solving the nonlinear si... [more] | IT2021-43 SIP2021-51 RCS2021-211 pp.87-92 |
| RCS, SIP, IT | 2022-01-20 16:15 |
Online | Online | [Invited Talk]
When Deep Unfolding Meets Control Engineering Masaki Ogura (Osaka Univ.) IT2021-45 SIP2021-53 RCS2021-213 |
Deep unfolding is a technique for tuning parameters for accelerating the convergence of iterative algorithms, and has be... [more] | IT2021-45 SIP2021-53 RCS2021-213 p.95 |
| RCS, SIP, IT | 2022-01-21 10:55 |
Online | Online | A Study on Bayesian Receiver Design via Deep Unfolding-Aided Bilinear Inference for Correlated Large MIMO Ryota Tamaki, Kenta Ito, Takumi Takahashi (Osaka Univ.), Shinsuke Ibi (Doshisha Univ.), Seiichi Sampei (Osaka Univ.) IT2021-65 SIP2021-73 RCS2021-233 |
This paper proposes a joint channel and data estimation (JCDE) scheme via deep unfolding (DU)-aided bilinear generalized... [more] | IT2021-65 SIP2021-73 RCS2021-233 pp.207-212 |
| RCS, SIP, IT | 2022-01-21 11:20 |
Online | Online | Deep-Unfolded Sparse Signal Recovery Algorithm using TopK Operator Masanari Mizutani (NITech), Satoshi Takabe (TITech), Tadashi Wadayama (NITech) IT2021-72 SIP2021-80 RCS2021-240 |
Compressed sensing for estimating sparse signals is formulated as an NP-hard problem, where LASSO based on convex relax... [more] | IT2021-72 SIP2021-80 RCS2021-240 pp.245-251 |
| RCS, SIP, IT | 2022-01-21 15:00 |
Online | Online | [Invited Talk]
Deep Learning-Aided Belief Propagation for Large Multiuser MIMO Detection Takumi Takahashi (Osaka Univ.), Shinsuke Ibi (Doshisha Univ.), Seiichi Sampei (Osaka Univ.) IT2021-80 SIP2021-88 RCS2021-248 |
With the increasing dimensionality of wireless communication signals, low-complexity signal detection algorithms to solv... [more] | IT2021-80 SIP2021-88 RCS2021-248 pp.289-294 |
| RISING (3rd) |
2021-11-17 11:00 |
Tokyo | (Primary: On-site, Secondary: Online) |
A study on variable step size diffusion LMS algorithm with deep unfolding Tetsuya Sasaki (Osaka City Univ.), Kazunori Hayashi (Kyoto Univ.) |
In adaptive filtering using a fixed step size LMS (Least-Mean-Square), there is a trade-off between convergence speed an... [more] | |
| AP, RCS (Joint) |
2021-11-11 10:55 |
Nagasaki | NBC-Bekkan (Nagasaki) (Primary: On-site, Secondary: Online) |
A Study on Receive Beamforming for Multi-User Detection with Trainable Gaussian Belief Propagation Takanobu Doi, Jun Shikida, Kazushi Muraoka, Naoto Ishii (NEC), Daichi Shirase, Takumi Takahashi (Osaka Univ.), Shinsuke Ibi (Doshisha Univ.) RCS2021-159 |
We propose two digital receive beamforming (BF) methods for low-complexity and high-accuracy uplink signal detection via... [more] | RCS2021-159 pp.86-91 |
| RCS, SR, NS, SeMI, RCC (Joint) |
2021-07-14 10:30 |
Online | Online | Deep-Unfolding Aided Optimization of Edge Weights and Step Sizes for Diffusion LMS Algorithm Yuto Nishihata, Koji Ishii (Kagawa Univ.) RCC2021-22 |
This study proposes a deep-unfolding aided parameter setting for a diffusion LMS algorithm. Distributed signal processin... [more] | RCC2021-22 pp.1-6 |
| RCS, SR, NS, SeMI, RCC (Joint) |
2021-07-14 10:55 |
Online | Online | Relaxation of Network Restriction for Deep Learning Based Consensus Problem with Eigenvector Centrality Shoya Ogawa, Koji Ishii (Kagawa Univ.) RCC2021-23 |
he convergence performance of consensus problems depends on the applied weighting factors into individual edges. Unfortu... [more] | RCC2021-23 pp.7-12 |
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