9 citations · 21 across the 9 of their papers we have counts for
11 papers
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…
Theoretical Interpretation of Learned Step Size in Deep-Unfolded Gradient Descent
Satoshi Takabe, Tadashi Wadayama
Deep unfolding is a promising deep-learning technique in which an iterative algorithm is unrolled to a deep network architecture with trainable parameters. In the case of gradient…
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…
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…
Compute-and-forward relaying with LDPC codes over QPSK scheme
Satoshi Takabe, Tadashi Wadayama, Ángeles Vazquez-Castro +1
In this paper, we study a compute-and-forward (CAF) relaying scheme with low-density parity-check (LDPC) codes, a special case of physical layer network coding, under the quadratur…
Complex Trainable ISTA for Linear and Nonlinear Inverse Problems
Satoshi Takabe, Tadashi Wadayama, Yonina C. Eldar
Complex-field signal recovery problems from noisy linear/nonlinear measurements appear in many areas of signal processing and wireless communications. In this paper, we propose a t…