Research on Coding for Large-Scale Sensor Networks
| Project/Area Number |
18K04132
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 21020:Communication and network engineering-related
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| Research Institution | Gifu University |
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Principal Investigator |
LU SHAN 岐阜大学, 工学部, 助教 (30755385)
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| Co-Investigator(Kenkyū-buntansha) |
程 俊 同志社大学, 理工学部, 教授 (00388042)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2020: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
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| Keywords | 大規模ネットワーク / 逐次干渉除去 / マルチユーザ符号 / 深層学習 / Signature符号 / unsourced random access / signature code / spares recovery / User identification / Channel estimation / DNN based decoder / Multiple-access channel / random access, / multiuser coding, / spatial coupling, / 大規模センサネットワーク, / 圧縮センシング, / ランダムアクセス / 大規模センサーネットワーク / 共通符号 |
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| Outline of Final Research Achievements |
This research project aims to construct a coding method and decoding algorithm for large-scale sensor networks and evaluate their characteristics.
For coding methods in multiple access channels, (1) an algebraic coding method and (2) successive interference cancellation-based coding schemes are proposed to obtain a code with a higher transmission rate and performance.
Furthermore, as a decoding method for the multiple access channel, we proposed (3) an iterative decoding method based on compressed sensing and (4) the iterative decoding method based on neural network learning to improve the decoding performance.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究課題の遂行により、massive machine-type 通信システムの符号化及び復号の構成に貢献した。代数的な符号化方法の提案は、従来手法と比較して総符号化率を向上されてしており、圧縮センシングを基づいて繰り返す復号の方法は、単純な圧縮センシングによりが多くな性能改善を達成した。同時に、mMTC通信路に対して、実用な深層学習のアルゴリズム設計の有力なアプローチであることが示された。本研究で確立した符号化及び復号方法は、今後のmassive machine-type 通信システムに貢献になっていると考えている。
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Report
(5 results)
Research Products
(41 results)
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[Presentation] Optimal power allocation of superposition-coded relaying with finite-blocklength transmission over quasi-static Rayleigh channels2020
Author(s)
M. Kambara, G. Song, T. Kimura, and J. Cheng
Organizer
2020 Int. Symposium on Information Theory and Its Applications (ISITA2020), pp.534-538, 24-27, Oct. 2020, (Online), Kapolei, Aulani, Hawaii, USA.
Related Report
Int'l Joint Research
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[Presentation] Partial access for LDPC-coded-IDMA systems2019
Author(s)
A. Osamura, G. Song, T. Kimura, and J. Cheng
Organizer
Proc. the 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 6 pages, September 8-11, 2019, Turkey, Istanbul.
Related Report
Int'l Joint Research
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