Mitsubishi electric corporation (20240259062). COMMUNICATION SYSTEM simplified abstract

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COMMUNICATION SYSTEM

Organization Name

mitsubishi electric corporation

Inventor(s)

Tadahiro Shimoda of Tokyo (JP)

Mitsuru Mochizuki of Tokyo (JP)

Naofumi Iwayama of Tokyo (JP)

Tatsuya Tokuda of Tokyo (JP)

COMMUNICATION SYSTEM - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240259062 titled 'COMMUNICATION SYSTEM

Simplified Explanation:

The patent application describes a communication system that involves a communication terminal and a base station. The communication terminal encodes data using an encoding model, while the base station decodes the data using a decoding model. The encoding model encodes channel state-related data, and the base station uses machine learning to generate the encoding and decoding models based on learning data, including the channel state-related data.

Key Features and Innovation:

  • Communication system with encoding and decoding models for data transmission.
  • Encoding model encodes channel state-related data.
  • Base station uses machine learning to generate models based on learning data.
  • Models are used for encoding and decoding data between communication terminal and base station.

Potential Applications: This technology can be applied in various communication systems, such as wireless networks, IoT devices, and satellite communications.

Problems Solved:

  • Efficient encoding and decoding of data in communication systems.
  • Adaptation to changing channel states for improved data transmission.
  • Utilization of machine learning for model generation in communication systems.

Benefits:

  • Enhanced data transmission efficiency.
  • Improved communication reliability.
  • Adaptability to changing channel conditions.
  • Potential for increased data throughput.

Commercial Applications: The technology can be utilized in telecommunications companies, IoT device manufacturers, satellite communication providers, and any industry requiring reliable and efficient data transmission.

Prior Art: Readers can explore prior art related to machine learning in communication systems, encoding and decoding models, and adaptive data transmission techniques.

Frequently Updated Research: Researchers are constantly exploring advancements in machine learning for communication systems, adaptive encoding and decoding techniques, and optimization of data transmission in varying channel conditions.

Questions about Communication Systems: 1. How does machine learning improve data transmission in communication systems? 2. What are the key differences between encoding and decoding models in communication technology?


Original Abstract Submitted

a communication system includes: a communication terminal that encodes transmission data by using an encoding model that encodes and outputs data that has been input; and a base station that decodes data encoded with the encoding model by using a decoding model that, when encoded data is input, decodes and outputs the data, the encoding model performs encoding on channel state-related data that is data on a channel state between the communication terminal and the base station, and the base station executes machine learning using learning data including the channel state-related data in an un-encoded state to generate the encoding model and the decoding model, and notifies the communication terminal of a learning result of the encoding model.