| 研究実績の概要 |
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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