User Equipment Network Measurement Request for ML Training
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Solution Overview
Problem
Current wireless communication systems lack efficient mechanisms for network-aided training and inference of machine learning models at user equipment (UE) to enhance network performance, leading to inefficiencies in data collection and processing.
Innovation Solution
A method and apparatus that enable user equipment (UE) to request and receive network measurements from network entities within a wireless communications network, allowing for local training and inference of machine learning models using collected data, which can include historical or real-time data from various network functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wireless communication networks collect information to determine network performance, then network performance monitoring is improved, but information collection techniques are deficient
Solution Approach 1:
The UE performs machine learning model training and inference operations locally using network measurement information, enabling the device to serve itself with intelligent processing capabilities rather than relying entirely on network-side processing
Solution Approach 2:
The patent introduces a new dimension of operation by enabling ML model training and inference at the UE side, transforming the traditional network-centric architecture into a distributed intelligence system where processing occurs at multiple levels (network and device)
2Reliability
If machine learning model training and inference are performed at UE using network measurements, then network performance is enhanced, but signaling overhead increases
Solution Approach 1:
The patent extracts only the necessary measurement information from the network and transmits it to the UE, while the computationally intensive ML training and inference operations are performed locally at the UE, reducing the need for continuous signaling exchanges
Solution Approach 2:
The UE performs ML model training in advance using collected network measurement data, so that the model is ready for inference operations without requiring real-time network interaction during actual inference, reducing signaling overhead during operation
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may communicate with a network entity within a wireless communications network. The UE may transmit a request for information to the network entity and, in response to the request, the UE may receive the requested information from the network entity. For example, the UE may request data from one or more data repositories associated with the network entity. In some examples, the information request may be associated with one or more measurements associated with operations of the network. In some instances, the UE may use a machine learning model to perform training or inference based on the information associated with the one or more measurements.