Time-Domain Beam Prediction for Low-Latency Wireless Beam Updates
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies in beam management due to high signaling overhead and latency in updating beam configurations, particularly in high-frequency scenarios, leading to potential beam failure and increased energy consumption.
Innovation Solution
Implementing wireless device-sided time domain beam predictions using AI/ML models to autonomously update beam configurations at predetermined time instances based on historical measurements, reducing the need for immediate network feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional network-controlled beam updating is used, then beam configuration can be dynamically adjusted, but signaling overhead and latency increase
Solution Approach 1:
The system performs preliminary beam predictions using AI/ML models before actual beam updates are needed. The network node pre-calculates optimal beam configurations based on historical data and device movement patterns, so that when beam updates are required, they can be applied immediately without waiting for extensive real-time measurements and signaling exchanges.
Solution Approach 2:
The wireless device is empowered to autonomously select and apply beam configurations based on predictions provided by the network. Instead of the network continuously commanding beam changes, the device uses local AI/ML models to self-determine optimal beams, reducing the need for constant network control signaling while maintaining adaptability.
2Measurement precision
If frequent beam measurements and updates are performed, then beam accuracy is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous beam measurements and updates, the system implements periodic beam predictions at predetermined time instances. The network node requests beam information at specific intervals, and AI/ML models predict future beam states based on historical patterns, allowing the device to conserve energy by not performing measurements continuously while still maintaining adequate beam accuracy.
Solution Approach 2:
The system uses lightweight AI/ML models that can be deployed on wireless devices with limited computational resources. These models provide sufficient prediction accuracy for beam selection without requiring extensive processing power or continuous operation, enabling energy-efficient beam management on resource-constrained devices.
3Measurement precision
If AI/ML models are deployed at wireless device, then beam prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The network node generates beam predictions using sophisticated AI/ML models and then transmits these predictions to the wireless device. The device receives pre-computed beam information from the network, eliminating the need for the device to host complex prediction models locally. This copying approach maintains prediction accuracy while keeping device complexity low.
Solution Approach 2:
The network node acts as an intermediary that hosts the computationally intensive AI/ML models and performs beam predictions. It then communicates the results to the wireless device, which applies them without needing to run the models itself. This intermediary approach allows sophisticated predictions while keeping device complexity manageable.
Data Source
AI summary
A method, network node and wireless device (WD) for beam indications for WD-sided time domain beam predictions, are disclosed. According to one aspect, a method in a WD includes receiving from a network node a channel state information (CSI) reporting configuration indicating a first set of future time instances for CSI predictions to be reported by the WD. The method includes performing a time sequence of measurements on a set of reference signal beams transmitted by the network node. The method also includes transmitting a beam information report that includes CSI predictions the first set of future time instances, the CSI predictions based at least in part on the time sequence of measurements. The method further includes receiving a beam indication indicating at least one selected beam for the CSI predictions to be autonomously applied by the WD at a second set of future time instances.