AI-Based CSI Encoding Between Terminal and Base Station
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Solution Overview
Problem
The challenge lies in enabling the application of AI to encode and decode Channel State Information (CSI) reports in communication systems, as existing approaches lack disclosed encoding and decoding models, hindering the efficiency of AI-driven communication systems.
Innovation Solution
A communication system that employs an encoding model at the communication terminal and a decoding model at the base station, where the base station uses machine learning to generate these models based on channel state-related data and notifies the terminal of the learning results, allowing for efficient AI-driven encoding and decoding of CSI reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are applied to encode and decode CSI reports, then communication system efficiency is improved, but the complexity of the system increases due to the need for machine learning model generation and notification mechanisms
Solution Approach 1:
The base station performs self-learning by executing machine learning using learning data including channel state-related data to generate the encoding and decoding models autonomously, without requiring external intervention or manual configuration
Solution Approach 2:
The base station notifies the communication terminal of the learning result of the encoding model, creating a feedback mechanism that enables the terminal to use the appropriate model for encoding CSI reports, thereby improving overall system efficiency through coordinated AI-driven processing
Data Source
AI summary
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.