Search Results for author: Mikio Hasegawa

Found 5 papers, 0 papers with code

A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN

no code implementations3 Aug 2022 Aohan Li, Ikumi Urabe, Minoru Fujisawa, So Hasegawa, Hiroyuki Yasuda, Song-Ju Kim, Mikio Hasegawa

(1) Compared to other lightweight transmission-parameter selection schemes, collisions between LoRa devices can be efficiently avoided by our proposed scheme in LoRaWAN irrespective of changes in the available channels.

Fairness reinforcement-learning +1

Theory of Acceleration of Decision Making by Correlated Time Sequences

no code implementations30 Mar 2022 Norihiro Okada, Tomoki Yamagami, Nicolas Chauvet, Yusuke Ito, Mikio Hasegawa, Makoto Naruse

In this study, we demonstrate a theoretical model to account for accelerating decision-making by correlated time sequence.

Decision Making Time Series +1

Resource allocation method using tug-of-war-based synchronization

no code implementations19 Aug 2021 Song-Ju Kim, Hiroyuki Yasuda, Ryoma Kitagawa, Mikio Hasegawa

We propose a simple channel-allocation method based on tug-of-war (TOW) dynamics, combined with the time scheduling based on nonlinear oscillator synchronization to efficiently use of the space (channel) and time resources in wireless communications.


Arm order recognition in multi-armed bandit problem with laser chaos time series

no code implementations26 May 2020 Naoki Narisawa, Nicolas Chauvet, Mikio Hasegawa, Makoto Naruse

By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series.

Irregular Time Series Time Series +1

Scalable photonic reinforcement learning by time-division multiplexing of laser chaos

no code implementations26 Mar 2018 Makoto Naruse, Takatomo Mihana, Hirokazu Hori, Hayato Saigo, Kazuya Okamura, Mikio Hasegawa, Atsushi Uchida

In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time-series.

Decision Making reinforcement-learning +3

Cannot find the paper you are looking for? You can Submit a new open access paper.