AI MIMO Detector Adaptation via Learning Class Feedback
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Solution Overview
Problem
Deep learning-based AI algorithms in communication systems face challenges in adapting to diverse channel environments due to the need for extensive training data, and existing methods struggle to efficiently reflect performance improvements in online learning scenarios.
Innovation Solution
A method for determining and transmitting learning class information, which indicates the degree of performance improvement in online learning, allowing for adaptive changes in channel state information feedback to support performance enhancement in AI MIMO detectors and auto-encoders, enabling specification changes and standard operation definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If online learning is implemented to improve AI algorithm performance in communication systems, then system performance and adaptability are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the AI algorithm operation into distinct phases: offline learning phase where the AI model is trained with extensive training data, and online learning phase where the pre-trained model is deployed for real-time communication. This segmentation allows the complex training process to be separated from the communication system deployment, reducing implementation complexity while maintaining adaptability benefits.
Solution Approach 2:
The patent implements preliminary action by completing the AI algorithm training offline before deployment in the communication system. The offline learning phase prepares the AI model in advance with extensive training data, so that when deployed online, the system can directly utilize the pre-trained model without requiring complex real-time training capabilities, thus reducing device complexity.
2Measurement precision
If extensive training data is used for offline learning, then AI algorithm accuracy is improved, but loss of time and training duration increase
Solution Approach 1:
The patent applies preliminary action by performing extensive training data processing and model training in the offline phase before system deployment. This allows the AI algorithm to achieve high accuracy through comprehensive training without causing time loss during actual communication operations, as the training is completed in advance.
Solution Approach 2:
The patent segments the learning process into offline learning (using extensive training data for high accuracy) and online learning (for real-time adaptability). This segmentation allows the system to benefit from both extensive training data for accuracy and rapid online response for time efficiency.
3Productivity
If AI algorithms are deployed for real-time communication, then system performance is improved, but ease of operation and system control decrease
Solution Approach 1:
The patent implements feedback mechanisms where the online learning system continuously monitors communication channel conditions and performance metrics, then uses this feedback to adaptively adjust the AI model parameters in real-time. This feedback loop enables the system to maintain high performance while reducing operational complexity by automatically adapting to changing conditions without manual intervention.
Solution Approach 2:
The patent applies self-service by enabling the AI algorithm to automatically adapt and optimize its performance through online learning without requiring manual reconfiguration or complex control operations. The system serves itself by continuously learning from incoming data and adjusting its parameters, thereby improving productivity while maintaining ease of operation.
Data Source
AI summary
The present specification provides a method fir transmitting learning class information, the method being performed by a terminal in a wireless communication system and comprising the steps of: transmitting a random access (RA) preamble to a base station; receiving a random access response (RAR) from the base station in response to the RA preamble; performing a radio resource control (RRC) connection procedure with the base station; determining the learning class information, wherein the learning class information is information about the degree of performance improvement in online learning, and the online learning is artificial intelligence (AI) algorithm-based learning following the activation of the wireless communication system; transmitting the learning class information to the base station; and performing channel state information (CSI) feedback according to the learning class information.