Inter-Node Topology Signaling for ML-Based Beam Mobility
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Solution Overview
Problem
Wireless communications systems face challenges in maintaining optimal beamforming strategies due to the dynamic nature of wireless channels, leading to suboptimal performance if not continuously updated, especially in high-mobility environments or with fading effects.
Innovation Solution
Utilize machine learning (ML) models for predicting channel characteristics and topological information to improve beam and cell-level mobility, incorporating measurement resources, prediction target resources, and topological information for enhanced mobility procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional beamforming strategies are used without continuous updates, then system complexity is reduced, but beam prediction reliability deteriorates in dynamic wireless environments
Solution Approach 1:
The patent applies preliminary action by pre-configuring measurement resources, prediction target resources, and topological information before mobility procedures occur. The network entity prepares ML model inputs in advance, including measurement configurations and topological data about cells and beams, so that when mobility events occur, the predictions can be made quickly and reliably without complex real-time computations.
Solution Approach 2:
The patent introduces an intermediary ML model that acts as a mediator between raw measurement data and beamforming decisions. The ML model receives topological information and measurements as inputs, processes them through learned patterns, and outputs predicted channel characteristics, thereby simplifying the overall system complexity while improving prediction reliability.
2Reliability
If machine learning models are deployed for real-time channel prediction, then beam prediction reliability improves, but processing time increases
Solution Approach 1:
The patent reduces processing time by performing preliminary actions - the network entity pre-configures all necessary measurement resources, prediction target resources, and topological information before mobility procedures occur. This preparation ensures that when actual predictions are needed, the ML model has all required inputs ready, eliminating delays associated with real-time data collection and processing.
3Measurement precision
If comprehensive topological information is collected for all cells and beams, then prediction accuracy improves, but information processing complexity increases
Solution Approach 1:
The patent applies the extraction principle by selectively taking out only the most relevant topological information needed for ML-based predictions. The network entity identifies and extracts key topological relationships between cells and beams that are essential for prediction accuracy, rather than processing all possible topological data. This reduces information processing complexity while maintaining prediction accuracy.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for inter-node relationship information indication for artificial intelligence/machine learning (AI/ML)-based mobility. An example method, performed at a user equipment (UE), generally includes receiving signaling configuring the UE with (i) measurement resources, (ii) prediction target resources, and (iii) topological information for a set of cells and beams, and participating in mobility procedures involving a machine learning (ML) model and predictions for the prediction target resources, based on the topological information and measurements taken for the measurement resources.