Joint ML Model Training With Shared Components in Communication Nodes
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Solution Overview
Problem
Existing communication systems face challenges in managing and training multiple machine learning (ML) models with a common part, leading to tedious configurations, high system overhead, and lack of reliability in AI/ML-based CSI feedback and beam management.
Innovation Solution
Implement joint training of ML models with a common part using a training assistant information mechanism, facilitated by broadcast-like signaling, to reduce system signaling overhead and enhance communication performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple ML models are configured one by one in a straightforward manner, then each model can be individually managed, but the configuration process becomes very tedious and system overhead increases
Solution Approach 1:
The patent combines multiple ML model configurations into a unified configuration process. Instead of configuring each ML model separately, the system uses a single configuration message that can simultaneously configure multiple ML models with common parts, thereby reducing the tediousness of individual configuration and decreasing overall configuration time.
Solution Approach 2:
The patent creates a universal configuration mechanism that can handle multiple ML models with common parts through a single configuration interface. This multi-functional configuration approach allows the system to manage various ML models (for CSI feedback, beam management, positioning) using the same configuration procedure, improving ease of operation while reducing time loss.
2Reliability
If multiple ML models with common parts are trained separately, then each model can be independently optimized, but joint training rules and updates become complex and require extensive signaling
Solution Approach 1:
The patent merges the training process of multiple ML models with common parts into a joint training mechanism. By combining the training of encoder and decoder models simultaneously with coordinated weight updates, the system achieves reliable training while avoiding the complexity of separate training management and extensive signaling exchanges.
Solution Approach 2:
The patent implements a feedback mechanism in joint training where training status and weight update information are exchanged between nodes. This feedback loop enables coordinated updates of common parts across multiple ML models, ensuring training reliability while managing complexity through structured information exchange rather than uncoordinated separate training.
3Measurement precision
If comprehensive training information is exchanged between nodes, then joint training accuracy improves, but signaling overhead increases
Solution Approach 1:
The patent extracts and separates the common part configuration from individual ML model configurations. By identifying and extracting the common elements (encoder/decoder structures, shared weights) that need to be jointly trained, the system can exchange only the necessary training information for these common parts, thereby maintaining joint training accuracy while reducing overall signaling overhead.
Solution Approach 2:
The patent segments the ML model configuration into common parts and model-specific parts. This segmentation allows the system to handle joint training of common parts through coordinated signaling while managing model-specific configurations independently, thereby reducing the total amount of signaling overhead while preserving the accuracy benefits of joint training for shared components.
Data Source
AI summary
A method for configuring at least one first node training machine learning (ML) model includes being provided with a training assistant information by a second node, wherein the training assistant information is used for the first node to perform joint training with the second node to train a plurality of ML models having a common part.