17886425. USER EQUIPMENT DOWNLINK TRANSMISSION BEAM PREDICTION FRAMEWORK WITH MACHINE LEARNING simplified abstract (Nokia Technologies Oy)
USER EQUIPMENT DOWNLINK TRANSMISSION BEAM PREDICTION FRAMEWORK WITH MACHINE LEARNING
Organization Name
Inventor(s)
Frederick Vook of Schaumburg IL (US)
USER EQUIPMENT DOWNLINK TRANSMISSION BEAM PREDICTION FRAMEWORK WITH MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17886425 titled 'USER EQUIPMENT DOWNLINK TRANSMISSION BEAM PREDICTION FRAMEWORK WITH MACHINE LEARNING
Simplified Explanation
The patent application describes a framework for predicting downlink transmission beams for user equipment using machine learning.
- Indicating a beam prediction capability of a user equipment to a network.
- Receiving network antenna configuration identification based on the indicated beam prediction capability.
- Selecting a model for beam prediction based on the network antenna configuration identification.
- Receiving a configuration of reference signal resources.
- Measuring reference signals based on the configuration of reference signal resources.
- Performing beam prediction using the selected model and measured reference signals.
- Reporting the beam prediction to the network.
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- Potential Applications
- Mobile communication networks
- Internet of Things (IoT) devices
- Autonomous vehicles
- Problems Solved
- Improving network efficiency by predicting downlink transmission beams for user equipment.
- Enhancing communication reliability by optimizing beam selection based on network antenna configuration.
- Benefits
- Increased data transmission speeds
- Reduced network congestion
- Improved user experience with better connectivity and coverage.
Original Abstract Submitted
Systems, methods, apparatuses, and computer program products for a user equipment downlink transmission beam prediction framework with machine learning are provided. For example, a method can include indicating a beam prediction capability of a user equipment to a network. The method can also include receiving network antenna configuration identification responsive to the indicated beam prediction capability. The method can further include selecting a model for beam prediction based on the network antenna configuration identification. The method can additionally include receiving a configuration of reference signal resources. The method can also include measuring reference signals based on the configuration of reference signal resources. The method can further include performing beam prediction based on the measured references signals using the selected model. The method can additionally include reporting a beam prediction to the network.
