Adapting Reference Signal Patterns via Machine Learning
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Solution Overview
Problem
Current communication systems, particularly 5G NR, lack efficient mechanisms for adapting reference signal patterns based on the statistics of randomly-varying wireless channels, leading to suboptimal channel state information estimation and feedback.
Innovation Solution
The implementation of a framework that supports AI/ML techniques for selecting reference signal patterns, allowing user equipment (UE) to signal capability for machine learning adaptation to the base station, and configuring reference signal patterns using machine learning models and parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference signal patterns are fixed according to conventional methods, then system complexity is reduced, but channel state information estimation accuracy deteriorates
Solution Approach 1:
The patent implements dynamic reference signal pattern adaptation where the pattern configuration changes based on real-time channel conditions. The base station determines optimal RS patterns according to channel statistics and signals them to the UE, enabling the system to adapt to varying channel characteristics rather than using fixed patterns, thereby improving estimation accuracy while managing complexity through automated adaptation.
Solution Approach 2:
The patent changes the parameters of reference signal patterns (such as density, position, and configuration) based on channel state information and statistics. By dynamically adjusting these parameters according to channel conditions, the system achieves more accurate channel estimation without requiring complete system redesign, balancing accuracy improvement with acceptable complexity increase.
2Measurement precision
If reference signal patterns are dynamically adapted to channel statistics, then channel state information estimation accuracy is improved, but signaling overhead increases
Solution Approach 1:
The base station performs preliminary determination of optimal reference signal patterns based on channel statistics before actual data transmission. By pre-calculating and signaling the optimal pattern configuration in advance, the system avoids the need for extensive real-time signaling and feedback, reducing overhead while maintaining accurate channel estimation through proactive adaptation.
3Adaptability or versatility
If machine learning models are used for reference signal pattern selection, then adaptability to channel variations is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the base station autonomously performs machine learning-based pattern selection and adaptation without requiring complex processing at the UE side. The network side handles the computational complexity of ML model execution, while the UE simply follows the configured patterns, thereby achieving high adaptability while distributing complexity appropriately across the system.
Data Source
AI summary
Capability of a user equipment to support machine learning adaptation by a base station of a reference signal pattern is signaled between the base station and the user equipment. Configuration information from the base station indicates one or more of enabling or disabling of machine learning adaptation of the reference signal pattern, a machine learning model used for machine learning adaptation of the reference signal pattern, updated model parameters for the machine learning model, or whether model parameters received from the user equipment will be used for machine learning adaptation of the reference signal pattern. Model training may be performed or model parameters received, and reference signals are received from the base station. Information on a reference signal pattern may be transmitted by the user equipment to the serving base station. Assistance information may be transmitted by the user equipment to the base station, which configures a reference signal pattern.