7 papers
Factor-Wise Parameter Learning in Score-Based VAMP
Siqi Na, Tadashi Wadayama
Score-based vector approximate message passing (SC-VAMP) builds state-evolution-predictable inference from modular soft-input soft-output (SISO) factors, but every factor must be h…
Differentiable Conditional Mutual Information for Multi-Terminal Linear Gaussian Wireless Networks
Tadashi Wadayama, Siqi Na
The rate regions of multi-terminal Gaussian channels (multiple-access, broadcast, interference, relay) are delimited by conditional mutual informations among g…
LLM-Steered Power Allocation for Parallel QPSK-AWGN Channels
Tadashi Wadayama
Large language models (LLMs) are increasingly being explored as high-level decision modules in closed-loop systems, but their stochastic nature makes safe integration challenging.…
Three-Module SC-VAMP for LDPC-Coded Nonlinear Channels
Tadashi Wadayama, Takumi Takahashi
We propose a three-module extension of score-based VAMP (SC-VAMP) for signal recovery in nonlinear channels, where the received signal is obtained by applying a nonlinearity to a l…
Score-Based VAMP with Fisher-Information-Based Onsager Correction
Tadashi Wadayama, Takumi Takahashi
We propose score-based VAMP (SC-VAMP), a variant of vector approximate message passing (VAMP) in which the Onsager correction is expressed and computed via conditional Fisher infor…
Physics-Aware Decoding for Communication Channels Governed by Partial Differential Equations
Tadashi Wadayama, Koji Igarashi, Takumi Takahashi
Digital communication systems inherently operate through physical media governed by partial differential equations (PDEs). In this paper, we introduce a physics-aware decoding fram…
Physics-Aware Sparse Signal Recovery Through PDE-Governed Measurement Systems
Tadashi Wadayama, Koji Igarashi, Takumi Takahashi
This paper introduces a novel framework for physics-aware sparse signal recovery in measurement systems governed by partial differential equations (PDEs). Unlike conventional compr…