Wireless Sub-Task Models for Low-Latency Network ML Adaptation
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently utilizing machine learning models across different tasks and devices, particularly in adapting generic models to specific sub-tasks, such as those required for ultra-reliable and low-latency communications (URLLC) in 5G networks, due to the complexity and variability of wireless environments.
Innovation Solution
A method and apparatus for modifying sub-task specific models using a generic model trained by a second model training unit, incorporating training data from collectors, and transmitting the modified model to wireless nodes for performing machine learning-enabled tasks, enabling adaptation and tailoring of models for specific sub-tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a generic machine learning model is used across different tasks, then device complexity is reduced, but manufacturing precision deteriorates because the model cannot be optimized for specific sub-tasks
Solution Approach 1:
The patent divides the model training process into two segments: a generic model trained on diverse data for broad applicability, and sub-task specific models trained on task-specific data for optimized performance. This segmentation allows the system to maintain low complexity for general tasks while achieving high precision for specific sub-tasks.
Solution Approach 2:
The patent applies local quality by making different parts of the system have different model characteristics. The generic model provides baseline performance across all tasks, while sub-task specific models provide localized optimization for particular tasks. This allows each sub-task to have tailored model parameters and architecture suited to its specific requirements.
2Manufacturing precision
If sub-task specific models are trained for each task, then manufacturing precision is improved, but device complexity increases due to multiple models and training processes
Solution Approach 1:
The patent implements universality through the generic model that serves multiple sub-tasks. The generic model is trained on diverse data from multiple sources and can provide baseline performance across different tasks. This multi-functional approach reduces the need for completely separate models for each sub-task, thereby reducing overall system complexity.
Solution Approach 2:
The patent applies preliminary action by pre-training a generic model on diverse data before deploying it for specific sub-tasks. This pre-trained generic model serves as a foundation that can be quickly adapted to specific tasks with minimal additional training, reducing the complexity and time required for deploying task-specific models.
3Adaptability or versatility
If models are trained locally at each wireless node, then adaptability is improved, but loss of time increases due to distributed training processes
Solution Approach 1:
The patent applies preliminary action by pre-training the generic model centrally using diverse data from multiple sources before deployment. This pre-trained model can then be quickly adapted at individual wireless nodes with minimal additional training time, reducing the overall time loss while maintaining adaptability to local conditions.
Solution Approach 2:
The patent implements copying by distributing the pre-trained generic model to multiple wireless nodes. Instead of training separate models at each node, the system copies the generic model and performs lightweight local adaptation, significantly reducing training time while maintaining adaptability to specific sub-tasks and local environments.
4Loss of time
If a centralized model training approach is used, then loss of time is reduced through coordinated training, but adaptability deteriorates because the model cannot be customized for specific wireless nodes
Solution Approach 1:
The patent segments the model training into centralized generic model training and decentralized sub-task specific model adaptation. The centralized training of the generic model reduces time loss through coordinated resource allocation, while the decentralized adaptation allows each wireless node to customize the model for its specific sub-tasks and local conditions.
Solution Approach 2:
The patent applies local quality by allowing each wireless node to have customized sub-task specific models adapted to its local environment and requirements. This local customization is achieved through lightweight fine-tuning of the centrally trained generic model, maintaining both efficiency and adaptability.
Data Source
AI summary
According to an example embodiment, a method may include receiving, by a user equipment from a network node, a request for a sub-task specific model, including configuration parameters for the sub-task specific model, wherein the sub-task specific model is to perform or assist with performing a machine learning-enabled sub-constraints task; verifying the request for the sub-task specific model; modifying, by the user equipment, the sub-task specific model based on the trained generic model and the configuration parameters of the sub-task or the sub-task specific model; performing or executing, by the user equipment, a machine learning-enabled sub-task based on or using the modified sub-task specific model; and transmitting, by the user equipment to the network node, sub-task specific model outputs based on the performing or executing the machine learning-enabled sub-task based on or using the modified sub-task specific model.