UE Temporal Beam Prediction Using AI Models and CSI Reporting
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing beam management complexity and overhead, particularly in UE-centric AI/ML-based beam prediction, which requires improved methods for temporal beam prediction and reporting.
Innovation Solution
Implementing UE-centric AI/ML models for temporal beam prediction, utilizing RNN or DNN with fixed weights, and configuring CSI reporting settings to support beam quality prediction for multiple future instances, including CSI-RS resources and differential RSRP reporting to optimize beam management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional beam management methods are used, then system implementation is straightforward, but beam management complexity and overhead increase
Solution Approach 1:
The UE autonomously performs beam prediction using locally deployed AI/ML models without requiring complex network-side coordination. The UE self-services by collecting measurement data, training models, and generating beam predictions independently, thereby reducing overall system complexity while maintaining implementation feasibility through standardized interfaces
Solution Approach 2:
Traditional mechanical beam management procedures are replaced with AI/ML-based predictive modeling. Instead of relying on conventional algorithms and extensive signaling, the system uses neural networks to predict future beam states, significantly reducing management complexity and overhead while preserving ease of deployment through model-based abstraction
2Measurement precision
If AI/ML based beam prediction is implemented, then system performance and beam prediction accuracy improve, but beam management overhead increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing measurement data in advance for model training. The UE gathers historical measurement results and beam information beforehand, enabling accurate predictions without requiring excessive real-time signaling, thus improving accuracy while managing overhead
Solution Approach 2:
Instead of transmitting extensive raw measurement data, the system uses compact AI/ML models that capture essential patterns. The model parameters and predictions serve as compressed representations of complex measurement data, maintaining prediction accuracy while significantly reducing information overhead in communications
3Reliability
If comprehensive beam measurement and reporting is performed, then beam selection accuracy improves, but resource utilization efficiency decreases
Solution Approach 1:
The system performs partial measurements focused on key beam parameters rather than exhaustive measurements of all possible beams. By selectively measuring and reporting only the most relevant beam characteristics needed for accurate prediction, the system maintains beam selection accuracy while improving resource utilization efficiency
Solution Approach 2:
The system changes reporting parameters from detailed raw measurement data to compressed AI model predictions and key metrics. This parameter transformation enables accurate beam selection through model-based inference while significantly reducing the resources required for measurement and reporting operations
Data Source
AI summary
Methods and apparatuses for support of UE centric AI based temporal beam prediction are disclosed. In one embodiment, a UE comprises a transceiver; and a processor coupled to the transceiver, wherein the processor is configured to report, via the transceiver, a set of parameters to define each AI/ML Model that can be used for temporal beam prediction; and receive, via the transceiver, a configuration for CSI report setting for temporal beam prediction.