Two-Sided AI Model Training Across Multi-Vendor Wireless Systems
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Solution Overview
Problem
In a multi-vendor wireless ecosystem, training AI/ML models for wireless communications is challenging due to the need for encoders and decoders to learn interpretable information from each other, requiring solutions that address compatibility and synchronization across different vendor architectures while minimizing signaling overhead and maintaining proprietary designs.
Innovation Solution
The proposed solution involves various training types, including Training Type I (joint training at a single entity), Training Type II (joint training without model transfer), and Training Type III (sequential separate trainings), which allow for the training of two-sided AI/ML models across different entities, ensuring compatibility and performance while managing proprietary architectures and reducing information exchange overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If joint training is performed at a single entity (Training Type I), then reconstruction accuracy is improved, but device complexity and information security concerns increase
Solution Approach 1:
The patent segments the training process into separate encoder training and decoder training phases, allowing them to be performed at different entities independently. This segmentation reduces the complexity of coordinating joint training while maintaining reconstruction accuracy through sequential optimization.
Solution Approach 2:
The patent employs preliminary action by pre-training encoders at one entity and then using these pre-trained encoders to train decoders at another entity. This preliminary training step simplifies the overall training coordination while achieving high reconstruction accuracy.
2Loss of information
If separate training is performed at different entities (Training Type III), then information exchange overhead is reduced, but reconstruction accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where training results from one entity are transmitted to the other entity to guide subsequent training. This feedback loop allows separate training at different entities to achieve reconstruction accuracy comparable to joint training while minimizing information exchange overhead.
3Adaptability or versatility
If proprietary AI/ML models are used by different vendors, then vendor independence is maintained, but compatibility and synchronization between encoders and decoders become difficult
Solution Approach 1:
The patent establishes universal training protocols and interfaces that allow proprietary AI/ML models from different vendors to interoperate. By defining standardized training procedures and information exchange formats, the system maintains vendor independence while ensuring encoder-decoder compatibility across multi-vendor deployments.
Data Source
AI summary
Techniques pertaining to training artificial intelligence (AI)/machine learning (ML) models in wireless communications are described. An apparatus participates in training of a two-sided AI/ML model. The apparatus also performs a wireless communication by utilizing the two-sided AI/ML model.