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20182023
most citedTheoretical Interpretation of Learned Step Size in Deep-Unfolded Gradient Descent

9 citations · 25 across the 13 of their papers we have counts for

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17 papers · 1 filter

cs.IT20231 cited

Deep Unfolded Simulated Bifurcation for Massive MIMO Signal Detection

Satoshi Takabe

Multiple-input multiple-output (MIMO) is a key ingredient of next-generation wireless communications. Recently, various MIMO signal detectors based on deep learning techniques and…

cs.IT2021

Proximal Decoding for LDPC-coded Massive MIMO Channels

Tadashi Wadayama, Satoshi Takabe

We propose a novel optimization-based decoding algorithm for LDPC-coded massive MIMO channels. The proposed decoding algorithm is based on a proximal gradient method for solving an…

cs.IT2020

Acceleration of Cooperative Least Mean Square via Chebyshev Periodical Successive Over-Relaxation

Tadashi Wadayama, Satoshi Takabe

A distributed algorithm for least mean square (LMS) can be used in distributed signal estimation and in distributed training for multivariate regression models. The convergence spe…

cs.IT20201 cited

Deep Unfolded Multicast Beamforming

Satoshi Takabe, Tadashi Wadayama

Multicast beamforming is a promising technique for multicast communication. Providing an efficient and powerful beamforming design algorithm is a crucial issue because multicast be…

cs.IT20203 cited

Chebyshev Inertial Landweber Algorithm for Linear Inverse Problems

Tadashi Wadayama, Satoshi Takabe

The Landweber algorithm defined on complex/real Hilbert spaces is a gradient descent algorithm for linear inverse problems. Our contribution is to present a novel method for accele…

cs.IT20191 cited

Trainable Projected Gradient Detector for Sparsely Spread Code Division Multiple Access

Satoshi Takabe, Yuki Yamauchi, Tadashi Wadayama

Sparsely spread code division multiple access (SCDMA) is a promising non-orthogonal multiple access technique for future wireless communications. In this paper, we propose a novel…