| Project/Area Number |
21K14156
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 21020:Communication and network engineering-related
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| Research Institution | Japan Advanced Institute of Science and Technology |
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Principal Investigator |
LIU Lei 北陸先端科学技術大学院大学, 先端科学技術研究科, 助教 (80870906)
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| Project Period (FY) |
2021-04-01 – 2023-03-31
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| Project Status |
Discontinued (Fiscal Year 2022)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2024: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2023: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2022: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | Message Passing / Capacity Optimality / Channel Coding / Low Complexity / MMSE Optimality / Joint Detection-Decoding / Capacity optimal / Coded linear systems / Memory AMP / Low complexity / Encoding / Iterative detection / Decoding / Compressed sensing / Generalized MU-MIMO / Capacity Achieving / Detection |
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| Outline of Research at the Start |
Conventional multiuser MIMO with Gaussian input, IID channel or a few users has been well studied. However, these conditions are not satisfied in the practical wireless communications. Generalized multiuser MIMO with non-Gaussian input, non-IID channel and massive users is far from sloved, and will be the key scenario of the wireless communication beyond 5G. This project will derive a calculable capacity expression and provide a low-complexity capacity-achieving scheme for GMU-MIMO. This project will offer low-cost and high-spectral-efficiency solutions to the future cellular system.
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| Outline of Annual Research Achievements |
We made a major breakthrough in the matrix limitation of AMP and high-complexity limitation of orthogonal/vector AMP, firstly built a universal MAMP framework and proposed a low-complexity and minimum mean square error (MMSE)-optimal MAMP algorithm (see [1]). A coded OAMP is designed based on matched FEC coding [2], which achieves the information-theoretic limit of a large linear system with a right-unitarily-invariant matrix and non-Gaussian signaling. To address the above challenges in GMU-MIMO, we proposed a unified framework to accurately characterize the capacity and design a capacity optimal transceiver of GMU-MIMO, jointly considering encoding, modulation, detection, and decoding. We design a practical framework based on OAMP/VAMP to achieve the sum capacity of GMU-MIMO [3]. We published 5 IEEE journal papers (including 1 TIT, 1 TSP, 1 TCOM, 1 IoT Journal and 1 Access), and 3 IEEE ISIT conference papers.
[1] “Memory AMP,” IEEE TIT, 2022. [2] "Capacity optimality of OAMP: Beyond IID sensing matrices and Gaussian signaling," arXiv:2108.08503, 2021. [3] ``Constrained capacity optimal generalized multi-user MIMO: A theoretical and practical framework," IEEE TCOM, Dec. 2022.
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