WTRU Task-Based Regularization for Interoperable AI/ML Training
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently training interoperable AI/ML models for tasks such as channel state information (CSI) compression and estimation due to the lack of effective regularization techniques, leading to suboptimal performance and resource inefficiencies.
Innovation Solution
Implementing task-based regularization methods for AI/ML model training in wireless transmit/receive units (WTRUs), where WTRUs receive configuration information including tasks and performance metrics, allowing them to train models that meet predefined thresholds and adapt their performance, and communicate with networks using compressed CSI feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional AI/ML model training methods are used in wireless communication systems, then model training can be performed, but the training efficiency is low and interoperability between different vendors' models is poor
Solution Approach 1:
The patent introduces task-based regularization as a parameter change in the training process. By adding specific regularization terms to the loss function that enforce task-specific constraints and performance metrics, the training process is modified to produce models that are both efficient to train and interoperable across different vendors. The regularization parameters control the trade-off between training efficiency and interoperability.
Solution Approach 2:
The patent implements feedback mechanisms where performance metrics are continuously monitored during training and used to adjust the regularization terms. The network provides feedback on model performance, and this feedback is incorporated into the training process through the regularization framework, enabling iterative improvement of both training efficiency and interoperability.
2Measurement precision
If complex regularization techniques are applied to improve model performance, then model accuracy improves, but training complexity and computational resources increase
Solution Approach 1:
The patent segments the regularization process into distinct task-specific components. Instead of using a single complex regularization term, the approach divides the regularization into multiple simpler task-specific regularization terms, each addressing a particular aspect of model performance. This segmentation makes the training process more manageable and less complex while maintaining high accuracy.
Solution Approach 2:
The patent applies local quality by making the regularization task-specific rather than uniform across all model training. Each task (e.g., channel estimation, interference suppression) has its own tailored regularization terms that are optimized for that specific task's requirements. This localized approach improves accuracy for each task without requiring overly complex global regularization.
3Measurement precision
If more data is collected and transferred for model training to improve accuracy, then model performance improves, but data transfer overhead and training time increase
Solution Approach 1:
The patent applies preliminary action by performing task-based regularization during the model training phase rather than requiring extensive post-training data collection and transfer. The regularization terms incorporate task-specific constraints that guide the model learning process, reducing the need for additional data transfer and extended training time while maintaining high accuracy.
4Measurement precision
If vendor-specific AI/ML models are used to optimize individual vendor performance, then individual vendor model accuracy improves, but system-wide interoperability and compatibility deteriorate
Solution Approach 1:
The patent achieves universality by designing a vendor-agnostic task-based regularization framework that can be applied across different vendors' models. The regularization terms are defined based on universal task objectives (e.g., channel estimation, signal detection) rather than vendor-specific implementations. This allows models from different vendors to be trained with the same regularization approach, ensuring interoperability while maintaining the ability to achieve high accuracy on specific tasks.
Data Source
AI summary
A wireless transmit/receive unit (WTRU) may receive configuration information from a network. The configuration information may include a task associated with a WTRU-side model and a performance metric threshold related to the task. The WTRU may train a WTRU-side model for performing a use case based on regularization using the task. The WTRU may determine that the performance metric threshold is met by the WTRU-side model on the task. The WTRU may further send, to the network, an indication of the task based on the performance metric threshold being met by the WTRU-side model on the task.