ML Channel Information Transfer With Online Model Updating
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently transferring channel information between a base station and a terminal due to unknown channel states, leading to suboptimal signal restoration, and existing methods for channel information measurement and feedback are inefficient and result in high overhead.
Innovation Solution
A machine learning-based approach for channel information compression and decompression using a determined ML model, where the base station determines an ML model based on terminal capabilities, updates it through online training, and receives compressed channel information from the terminal, reducing overhead and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional channel information measurement methods are used, then channel state can be obtained, but the overhead is high and efficiency is low
Solution Approach 1:
The patent replaces traditional mechanical/channel-based information transfer methods with machine learning-based compression and decompression techniques. The ML model processes channel information in a computational domain, substituting the conventional signal processing approach with intelligent algorithms that achieve superior compression ratios and transfer efficiency while reducing overhead.
Solution Approach 2:
The patent changes the representation parameters of channel information by transforming raw channel state data into compressed representations through machine learning models. The ML model learns optimal parameter transformations that capture essential channel characteristics while discarding redundant information, thereby reducing the quantity of data transmitted without losing critical channel state information.
2Productivity
If machine learning model is updated through online training, then communication efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements online training through feedback mechanisms where the base station receives feedback information from the terminal about channel conditions and communication performance. This feedback is used to update the ML model parameters dynamically, allowing the system to adapt to changing channel conditions while continuously improving communication efficiency through iterative learning processes.
Solution Approach 2:
The patent introduces dynamic model updating capabilities where the ML model is not static but evolves over time through online training. The model adapts its parameters based on real-time channel conditions and communication experiences, transforming from a fixed structure to a dynamic system that automatically optimizes performance without requiring complete system redesign.
3Quantity of substance
If channel information is transferred with low overhead, then system resources are saved, but measurement precision may be compromised
Solution Approach 1:
The patent uses machine learning models to create compressed copies of channel information that retain the essential characteristics needed for effective communication. The ML model learns to create representative summaries or prototypes of channel state data that capture the most critical information patterns, allowing accurate channel representation with significantly reduced data volume compared to traditional methods.
Data Source
AI summary
A method of a base station may comprise: determining one of machine learning (ML) models for receiving channel information for a channel to communicate with a terminal based on capability information of the terminal; providing configuration information of the determined ML model to the terminal; updating the determined ML model through online training with the terminal; and receiving channel information using the updated ML model from the terminal when communicating with the terminal.