Group-Based AI Model Signaling for Multi-UE Network Overhead
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies in managing machine learning (ML) models across multiple user equipments (UEs), leading to increased network overhead and power consumption, as well as suboptimal resource allocation and model updating.
Innovation Solution
Implementing group-based management of ML models, where UEs are grouped based on compatible ML models, allowing for efficient group-based signaling for activation, deactivation, and updating of ML models, reducing network overhead and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual ML model management is implemented for each UE, then model accuracy and customization are improved, but network overhead and power consumption increase
Solution Approach 1:
The patent groups multiple UEs into UE groups based on their ML model characteristics and manages ML models at the group level rather than individually. The network unit transmits group-based indications to control plane UEs, which then distribute instructions to data plane UEs in the group. This merging approach reduces the number of separate signaling transmissions and model management operations, thereby reducing network overhead and power consumption while maintaining effective model management across multiple devices.
2Adaptability or versatility
If individual ML model management is implemented for each UE, then model customization is improved, but network overhead increases
Solution Approach 1:
The patent combines multiple UEs into groups that share common ML model characteristics, allowing the network to manage models at the group level rather than individually. This reduces the quantity of signaling messages and management operations required, thereby reducing network overhead. The group-based approach maintains adaptability by allowing different UE groups to have different ML models configured based on their specific requirements.
Solution Approach 2:
The patent introduces control plane UEs as intermediaries between the network unit and data plane UEs. The control plane UE receives group-based indications from the network unit and translates them into appropriate actions for the data plane UEs in the group. This intermediary layer enables efficient group-based management while maintaining the ability to customize models for different UE groups without requiring individual direct communication between the network and each UE.
3Productivity
If group-based ML model management is implemented, then network efficiency is improved, but individual UE model control may be reduced
Solution Approach 1:
The patent segments UE groups into control plane UEs and data plane UEs with distinct functional roles. The control plane UE handles receiving and processing group-based indications from the network, while data plane UEs execute the actual ML model operations. This segmentation allows efficient group-based management for network efficiency while maintaining individual UE control through the control plane UE's ability to process and distribute specific instructions to individual UEs within the group when needed.
Data Source
AI summary
Wireless communication devices, systems, and methods related to managing artificial intelligence (AI) and/or machine learning (ML) models are provided. For example, a method of wireless communication performed by a network unit may include transmitting, to one or more first user equipments (UEs), an indication of a first group identifier, wherein the first group identifier is based, at least in part, on one or more first machine learning (ML) models associated with the one or more first UEs; and transmitting, to at least one UE of the one or more first UEs, a first group-based signal associated with the first group identifier.