UE Model Update Grouping for Lower Telecom Broadcast Overhead
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Solution Overview
Problem
Machine learning models in telecommunications networks require frequent updates to maintain accuracy, but existing methods often transmit excess training data, leading to high overhead data transfer costs and resource consumption, and expose sensitive user information.
Innovation Solution
A system that assigns UE devices to update groups based on environmental and model parameters, optimizing the frequency of training data broadcasts to minimize overhead data transfer and ensure model performance, while maintaining privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data is frequently broadcast to UE devices to maintain model accuracy, then model performance is improved, but overhead data transfer costs and resource consumption increase
Solution Approach 1:
The patent segments UE devices into different update groups based on their model update needs and environmental factors. Each group receives training data at optimized frequencies, allowing the system to maintain model accuracy for devices that need it while reducing unnecessary data transfers for devices that don't require frequent updates, thereby resolving the contradiction between model performance and data transfer overhead.
Solution Approach 2:
The system dynamically adjusts the update frequency for each UE device based on real-time environmental parameters, device state, and model requirements. This dynamic adaptation allows the system to optimize the balance between maintaining model accuracy and minimizing data transfer overhead, as update frequencies are continuously adjusted rather than applied uniformly.
2Reliability
If training data is broadcast to all UE devices, then model performance is maintained, but computational and memory resource costs increase
Solution Approach 1:
The patent applies local quality by customizing the update strategy for each UE device based on its specific environmental parameters, device capabilities, and model requirements. Instead of applying a uniform update frequency to all devices, the system tailors the update approach to each device's local conditions, optimizing resource usage while maintaining necessary model performance.
Solution Approach 2:
By segmenting UE devices into different update groups with customized update frequencies and data selection strategies, the system reduces unnecessary computational and memory resource consumption on devices that don't require frequent or full model updates, while still maintaining model performance where needed.
3Reliability
If update frequency is increased for all devices, then model accuracy is maintained, but network traffic and server load increase
Solution Approach 1:
The system dynamically determines update frequencies for each UE device based on real-time environmental parameters, device state, and model performance requirements. This dynamic approach allows the network to optimize traffic patterns by increasing updates only when and where necessary, rather than applying a static high update frequency to all devices, thereby improving overall network efficiency while maintaining model accuracy.
Solution Approach 2:
By segmenting devices into update groups with different frequency requirements, the system reduces overall network traffic and server load by avoiding unnecessary updates to devices that don't require them, while ensuring adequate update frequency for devices that do need frequent updates to maintain accuracy.
4Manufacturing precision
If comprehensive training data is transmitted, then model update quality is improved, but data privacy risks increase
Solution Approach 1:
The patent extracts and transmits only the specific training data samples that are most relevant and necessary for each UE device's model update needs, based on environmental parameters and device-specific requirements. This selective extraction approach maintains model update quality by providing pertinent data while reducing privacy risks by minimizing the transmission of unnecessary sensitive information.
Solution Approach 2:
The system applies local quality by customizing the selection of training data for each UE device based on its specific context and requirements. Instead of transmitting comprehensive training data to all devices, the system tailors the data selection to each device's local needs, ensuring update quality while protecting privacy by transmitting only necessary information.
Data Source
AI summary
Systems and methods for configuring a network system to assign multiple user devices of a telecommunications network to a selected update group of a plurality of update groups. In some cases, the system comprises instructions to provide a data analysis model to the multiple user devices such that the data analysis model is usable by the multiple user devices to control performance of the multiple user devices, assign each of the multiple user devices to a selected update group of a plurality of update groups such that each respective user device is assigned to a corresponding selected update group based on a set of device environment parameters of the respective user device, detect an update event for the data analysis model, and selectively broadcast updated data samples for the data analysis model to user devices in a first update group of the plurality of update groups.