Local AI Model for Wireless Channel Noise Cancellation
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Solution Overview
Problem
Current 5G NR wireless communication systems lack effective AI/ML methods for canceling channel noise, which affects communication quality and data transmission reliability.
Innovation Solution
A method and apparatus for cancelling channel noise using AI/ML techniques in a wireless communication system, where a local AI model is configured based on an initial global AI model, trained with local data, and updated to improve noise cancellation performance tailored to individual channel environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI/ML methods are applied to cancel channel noise, then communication quality and data transmission reliability are improved, but device complexity increases due to model training and updating requirements
Solution Approach 1:
The AI model is divided into a global model managed by the base station and local models deployed at user equipment. The global model provides common noise cancellation functionality while local models handle device-specific channel characteristics. This segmentation reduces the complexity burden on individual devices while maintaining high reliability through collaborative intelligence.
Solution Approach 2:
The system implements a feedback mechanism where local models transmit update information to the base station, which then updates the global model. This feedback loop enables continuous improvement of noise cancellation performance across the network while distributing the complexity management between centralized and decentralized components.
2Adaptability or versatility
If a global AI model is used for channel noise cancellation, then adaptability to different channel environments is improved, but loss of time occurs during model updating and synchronization
Solution Approach 1:
By segmenting the model into global and local components, the system can update the global model periodically without requiring real-time synchronization across all devices. Local models continue operating with the latest global model version, reducing the impact of update delays on overall system adaptability.
Solution Approach 2:
The base station prepares and transmits global model updates in advance during idle periods or low-traffic times. This preliminary action allows user equipment to apply updates before they are needed for critical communications, minimizing the time loss during model synchronization.
3Manufacturing precision
If local data is used for training the AI model, then manufacturing precision of noise cancellation is improved for specific channels, but loss of information occurs during data transmission and model synchronization
Solution Approach 1:
The system segments data processing by keeping raw local channel data at user equipment for training purposes while only transmitting processed model update information to the base station. This segmentation minimizes the amount of data transmitted during synchronization, reducing information loss risks while maintaining precision through local data utilization.
Solution Approach 2:
Instead of transmitting all raw local data during synchronization, the system creates and transmits copies of only the essential model update parameters. This copying approach preserves the necessary information for maintaining noise cancellation precision while significantly reducing the data transmission burden and associated information loss risks.
Data Source
AI summary
The present disclosure relates to a technique for removing channel noise in a wireless communication system. A method of a UE may comprise: configuring a local AI model for channel noise cancellation of the UE based on an initial global AI model for channel noise cancellation; training the local AI model using local data received from a base station; generating local AI model update information based on the training of the local AI model; transmitting, to the base station, a first message including the local AI model update information; and receiving, from the base station, a first global AI model obtained by updating the initial global AI model.