RRC-Based AI Model Lifecycle Control in Wireless Networks

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Solution Overview

Problem

Existing wireless communication systems lack effective methods for managing the life cycle of AI/ML models, including training, deployment, inference, and performance monitoring, which hinders the efficient utilization of AI/ML capabilities in improving communication efficiency and accuracy.

Innovation Solution

Implementing Radio Resource Control (RRC) configuration messages to manage the lifecycle of AI/ML models by indicating the models to be used, receiving AI-based feedback, and performing activation, deactivation, and switching through network devices and wireless devices, utilizing RRC configuration messages to facilitate model training, deployment, and performance monitoring.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If AI/ML models are deployed in wireless communication systems, then communication efficiency and accuracy are improved, but the complexity of managing model lifecycle increases

Engineering Contradiction:

Improvecommunication accuracyVSAvoidmodel lifecycle management complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces RRC configuration messages as an intermediary mechanism between the network device and wireless device to manage AI/ML model deployment. These messages carry model identifiers, configuration parameters, and control instructions, serving as a standardized interface that simplifies the complex interactions required for model lifecycle management while enabling accurate AI-based communication

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If multiple AI/ML models are supported for different scenarios, then system adaptability is improved, but device complexity increases

Engineering Contradiction:

Improvesystem adaptabilityVSAvoidmodel management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the AI/ML model management into distinct components: model identification through unique identifiers, separate configuration parameters for different models, and individual activation/deactivation controls. This segmentation allows multiple models to be managed independently through standardized RRC messages, enabling system adaptability without proportionally increasing management complexity

Inventive Principle:

Principle #1Segmentation

3Productivity

If AI model training and deployment are implemented, then communication performance is improved, but loss of time in model management increases

Engineering Contradiction:

Improvecommunication efficiencyVSAvoidmodel management time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements preliminary action by pre-configuring AI/ML models with unique identifiers and parameters through RRC configuration messages before actual deployment. The network device can prepare model configurations in advance and activate them when needed, reducing the time required for model management operations during live communication operations

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260019344A1Life cycle management of ai/ML models in wireless comunication systems

Publication Date: 2026.01.15 APPLE INC

AI summary

Apparatuses, systems, and methods for life cycle management of AI/ML models in wireless communication systems. A wireless device comprising at least one antenna and a processor is configured to: receive a Radio Resource Control (RRC) configuration message from a network device, the RRC configuration message indicating an Artificial Intelligence (AI) model to be used by the wireless device; and transmit an AI-based feedback message generated based on the AI model to the network device.