Shigenori Tani

TL;DR: In this article, a deep learning-based method was proposed to improve the performance of SAGINs, where the authors analyzed several main challenges of SagINs and explained how these problems can be solved by AI.

TL;DR: In this paper, a mobile terminal judge whether or not the mobile terminal is able to make a transition to a DTX period during Active, and when judging that the mobile terminals is capable of making a transition, notifies a base station to that effect, which temporarily stops supply of electric power to the data transmission processing units and the data reception processing units.

TL;DR: In this article, the decision processing of making a decision as to whether the mobile terminal can receive reception-desired content it desires to receive among contents provided by the broadcasting communications service is executed in accordance with a frequency occupying bandwidth used for transmitting the reception desired content.

TL;DR: This article analyzes several main challenges of SAGINs and explains how these problems can be solved by AI, and considers the satellite traffic balance as an example and proposes a deep learning based method to improve traffic control performance.

TL;DR: In this paper, a mobile terminal judges whether or not the mobile terminal is able to make a transition to a DTX period during Active, and, when judging that the mobile terminals is capable of making a transition, notifies a base station to that effect, the base station temporarily stops supply of electric power to the data transmission processing units and the data reception processing units.