T
Toshiyuki Tanaka
Researcher at Kyoto University
Publications - 287
Citations - 7466
Toshiyuki Tanaka is an academic researcher from Kyoto University. The author has contributed to research in topics: Multiuser detection & Decoding methods. The author has an hindex of 42, co-authored 279 publications receiving 6854 citations. Previous affiliations of Toshiyuki Tanaka include Aston University & Tokyo Metropolitan University.
Papers
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A statistical-mechanics approach to large-system analysis of CDMA multiuser detectors
TL;DR: The performance of uncoded, fully synchronous, randomly spread code-division multiple-access (CDMA) multiuser detectors with additive white Gaussian noise (AWGN) channel, under perfect power control, and in the large-system limit is evaluated.
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Performance of polar codes with the construction using density evolution
Ryuhei Mori,Toshiyuki Tanaka +1 more
TL;DR: This letter evaluates performance of polar codes designed with the new construction method, and compares it with that of the codes constructed with another heuristic method with linear complexity proposed by Arikan.
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Antibacterial Activity of Extracts Prepared from Tropical and Subtropical Plants on Methicillin-Resistant Staphylococcus aureus
Tomoko Nitta,Takashi Arai,Hiromu Takamatsu,Yuka Inatomi,Hiroko Murata,Munekazu Iinuma,Toshiyuki Tanaka,Tetsuro Ito,Fujio Asai,Iriya Ibrahim,Tsutomu Nakanishi,Kazuhito Watabe +11 more
TL;DR: The antibacterial activity of the extracts prepared from 181 species of tropical and subtropical plants was screened against various types of pathogenic bacteria and Hemsleyanol d, one of the stilbene tetramer isolated from S. hemsleyana, was the most effective compound and had MIC of 2 μg/ml.
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A General Formula for the Stationary Distribution of the Age of Information and Its Application to Single-Server Queues
TL;DR: A general formula is derived that the stationary distribution of the AoI is given in terms of the stationary distributions of the system delay and the peak AoI, which holds for a wide class of information update systems.
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A User's Guide to Compressed Sensing for Communications Systems
TL;DR: The problem of compressed sensing is considered as an underdetermined linear system with a prior information that the true solution is sparse, and the sparse signal recovery is explained based on � 1 optimization, which plays the central role in compressed sensing.