AI Model Management Between Wireless Radio Nodes
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Solution Overview
Problem
Current wireless communication networks lack effective management of Artificial Intelligence (AI)/Machine Learning (ML) models across radio nodes, leading to inefficient model selection, performance supervision, and retraining needs.
Innovation Solution
Introducing communication between radio nodes to manage AI/ML models implemented at User Equipment (UE) and network nodes, enabling optimized model selection, performance monitoring, and tailored model usage based on conditions such as signal quality and node energy state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If AI/ML models are deployed at radio nodes without inter-node communication for model management, then device complexity is reduced and ease of operation is improved, but model performance supervision and retraining coordination deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where radio nodes exchange model performance information and status updates with neighboring nodes. This allows the network to monitor model performance across distributed nodes and trigger retraining when performance degradation is detected, resolving the contradiction between operational simplicity and performance supervision.
Solution Approach 2:
The patent introduces an intermediary model management function that coordinates between distributed radio nodes. This intermediary layer handles model distribution, performance collection, and retraining coordination, enabling reliable model supervision without requiring complex direct peer-to-peer management between all nodes.
2Ease of manufacture
If AI/ML models are managed independently at each radio node, then device complexity is reduced and ease of manufacture is improved, but adaptability to network conditions deteriorates
Solution Approach 1:
The patent implements dynamic model selection where radio nodes can adaptively choose from multiple model versions based on current network conditions, performance metrics, and node-specific characteristics. This dynamic approach allows the system to be manufactured with a set of models but adaptively select the most appropriate model at runtime, resolving the contradiction between ease of manufacture and adaptability.
Solution Approach 2:
The patent segments the model management function into independent components that can be distributed across radio nodes. Each node maintains local model information and can independently select appropriate models, while still benefiting from network-wide model coordination. This segmentation enables simple manufacturing of individual nodes while achieving collective adaptability through their interaction.
3Reliability
If comprehensive model management communication is implemented between radio nodes, then model performance supervision and retraining coordination are improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent implements periodic model performance reporting and status exchange between radio nodes, rather than continuous communication. Nodes report model performance metrics and status at predetermined intervals or when significant changes occur, enabling reliable model supervision while minimizing communication energy consumption compared to continuous monitoring.
4Reliability
If frequent model updates and retraining are coordinated between radio nodes, then model performance and reliability are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent implements preliminary model training and preparation at centralized locations or during low-traffic periods, so that trained models are ready for deployment before needed. This preliminary action allows the system to maintain high model performance and reliability through regular updates while minimizing the time loss during actual model deployment, as the models are pre-trained and ready for quick distribution to radio nodes.
Data Source
AI summary
A method (1100) by a radio node (110, 210, 310) includes transmitting (1102), to another radio node (120, 220, 320), information indicating an activation or a deactivation of one or more AI and/or ML models at the radio node. For example, the radio node may include a UE and the other radio node may include a base station such that the base station is able to inform and/or suggest modifications in the node configurations to enhance communication performance and model selection at the UE.