Cellular ML-Model Updates with RRC State Coordination
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Solution Overview
Problem
Existing methods for handling Machine Learning (ML) model updates in cellular networks, particularly involving both User Equipment (UE) and network nodes, can cause performance degradation or connectivity loss due to unsynchronized updates, especially when UEs transition between RRC states.
Innovation Solution
A method where UEs and network nodes communicate through RRC, MAC CE, Msg1/3, UCI, or SCI messages to request, confirm, and update ML-models, ensuring updates occur during optimal network states like RRC_IDLE or RRC_INACTIVE to minimize disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ML-model updates are performed without coordination between UE and network node, then update flexibility is improved, but connectivity reliability deteriorates due to unsynchronized updates causing performance degradation or connectivity loss
Solution Approach 1:
The network node sends a notification message to the UE in advance before initiating the ML-model update. This preliminary action allows the UE to prepare for the update by transitioning to an idle or inactive state, ensuring that the update will not disrupt ongoing communications. The advance notification enables coordinated updates while maintaining connectivity reliability.
Solution Approach 2:
The UE sends an acknowledgment message back to the network node to confirm receipt of the update notification. This feedback mechanism ensures that both parties are synchronized about the update status, allowing the network to track which UEs have been notified and can coordinate the actual update timing to maintain connectivity reliability.
2Speed
If ML-model updates are performed during active communication states, then update speed is improved, but communication stability deteriorates due to potential disruptions to ongoing communications
Solution Approach 1:
The system transitions the UE to an idle or inactive state before performing the ML-model update. This preliminary state change ensures that no ongoing communications are disrupted during the update process. The update is then performed at high speed in the idle state, and communications resume normally after update completion, maintaining both speed and stability.
3Productivity
If ML-model updates are performed without state transition, then update efficiency is improved, but connectivity continuity deteriorates due to unsynchronized updates during state transitions
Solution Approach 1:
The network node proactively notifies the UE of the upcoming ML-model update before the UE transitions between RRC states. This allows the update to be scheduled and executed during an idle or inactive state, ensuring that the update completes before the UE returns to an active communication state. This approach maintains connectivity continuity while achieving efficient updates.
4Reliability
If ML-model updates are coordinated through messaging protocols, then connectivity reliability is improved, but signaling overhead increases due to additional RRC, MAC CE, or physical layer messages
Solution Approach 1:
The notification and acknowledgment messages use existing RRC, MAC CE, or physical layer signaling protocols that are already part of the cellular communication standard. By reusing these established messaging mechanisms for ML-model update coordination, the system achieves reliable connectivity without requiring entirely new signaling protocols, thereby limiting the increase in signaling overhead to only what is necessary for the update coordination.
Data Source
AI summary
Disclosed are methods, apparatuses, and systems for using Artificial Intelligence (AI) and Machine Learning (ML) in cellular networks. In one aspect, a method is performed by a first node. The method includes sending, to a second node, a first message that indicates a request to update or reconfigure a functionality in the first node related to an ML-model or another functionality in which the ML-model is a part. The method further includes receiving, from the second node, a second message responsive to the first message, and performing an update of the functionality related to the ML-model based on the second message.