Reference Signal Imputation for Single-Model Wireless Parameter Estimation
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Solution Overview
Problem
Existing machine learning (ML) models for wireless communication parameter estimation require re-training when input data dimensions change, leading to high complexity and resource usage due to the need for multiple models to accommodate different reference signal configurations.
Innovation Solution
A method and apparatus for wireless communication that involves transforming reference signals of a first type into input signals of a second type, enabling the use of a single ML model for parameter estimation by user equipment (UE), allowing adaptation to different configurations without re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple ML models are trained for different reference signal configurations, then parameter estimation accuracy is maintained, but device complexity and resource usage increase significantly
Solution Approach 1:
The patent applies universality by designing a single ML model architecture that can process multiple reference signal configurations through configurable parameters. Instead of training separate models for each configuration, the system uses one universal model that adapts to different RS types (CSI-RS, SRS, DMRS, PTRS, BRS, BRRS) and dimensions through parameter settings, thereby reducing device complexity while maintaining estimation accuracy
Solution Approach 2:
The patent implements parameter changes by allowing the ML model to accept different input configurations through parameter adjustment rather than structural modification. The model can handle varying RS dimensions and types by changing input parameters and configuration settings, eliminating the need to train and store multiple specialized models
2Adaptability or versatility
If ML models are re-trained for every reference signal configuration change, then adaptation to new configurations is achieved, but loss of time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring the ML model with the capability to handle multiple reference signal types and dimensions during the initial model design phase. The model is prepared in advance with configurable parameters that allow it to adapt to different RS configurations without requiring re-training, thus eliminating time loss when new configurations are introduced
Solution Approach 2:
The universal model design allows the system to adapt to new reference signal configurations immediately by parameter adjustment rather than re-training. The model was preliminarily designed to be versatile, covering multiple RS types and dimensions, so when new configurations are needed, the system can adapt through parameter changes without time-consuming re-training processes
3Measurement precision
If multiple ML models are stored for different reference signal dimensions, then accurate estimation for all configurations is possible, but use of energy and storage resources increase
Solution Approach 1:
The patent reduces energy and storage usage by replacing multiple specialized ML models with a single universal model. This universal model can process any reference signal configuration through parameter adjustments, eliminating the need to store and load multiple model instances. The result is significant reduction in memory usage and energy consumption while maintaining the same estimation accuracy for all RS types and dimensions
Data Source
AI summary
Disclosed is a method for wireless communication. The method comprises receiving, from a network node, a reference signal (RS) of a first type. An input signal of a second type is determined based on the reference signal. Signals of the second type are associated with training a first machine learning (ML) model applied by a UE to estimate a parameter used in wireless communications by the UE. The input signal of the second type is applied to the first ML model to estimate the parameter used in wireless communications by the UE.