no code implementations • 18 Mar 2021 • Ryo Hayakawa
In this paper, we first analyze the asymptotic property of the proximity operator for the squared loss function, which appears in the update equations of some proximal splitting methods for linear inverse problems.
no code implementations • 28 Sep 2020 • Ryo Hayakawa
We also show that, by using the proposed method, we can tune the regularization parameter of the $\ell_{1}$ optimization to achieve good reconstruction performance even when the noise variance is unknown.
1 code implementation • 17 Sep 2020 • Ryo Hayakawa
The derivation of the proposed method is based on the recently developed convex Gaussian min-max theorem (CGMT), which can be applied to various convex optimization problems to obtain its asymptotic error performance.
1 code implementation • 25 Dec 2018 • Satoshi Takabe, Masayuki Imanishi, Tadashi Wadayama, Ryo Hayakawa, Kazunori Hayashi
This paper presents a deep learning-aided iterative detection algorithm for massive overloaded multiple-input multiple-output (MIMO) systems where the number of transmit antennas $n$ is larger than that of receive antennas $m$.
