18048728. MACHINE LEARNING BASED TIMING ADVANCE UPDATES simplified abstract (QUALCOMM Incorporated)
Contents
- 1 MACHINE LEARNING BASED TIMING ADVANCE UPDATES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING BASED TIMING ADVANCE UPDATES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MACHINE LEARNING BASED TIMING ADVANCE UPDATES
Organization Name
Inventor(s)
Tianyang Bai of Somerville NJ (US)
Hua Wang of Basking Ridge NJ (US)
Junyi Li of Fairless Hills PA (US)
MACHINE LEARNING BASED TIMING ADVANCE UPDATES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18048728 titled 'MACHINE LEARNING BASED TIMING ADVANCE UPDATES
Simplified Explanation
The patent application describes methods, systems, and devices for wireless communications at a user equipment (UE) using machine learning models to predict timing advance values for uplink communications.
- The UE identifies measurement parameters such as reference signal received power measurements or past timing advance values for input into a machine learning model.
- The UE predicts the timing advance based on the input measurement parameters and transmits an uplink communication to a network entity using the predicted timing advance.
- The UE may transmit a capability report to indicate support for autonomous update of the timing advance based on predictions from the machine learning model.
Potential Applications
This technology could be applied in various wireless communication systems to improve the efficiency and reliability of uplink transmissions.
Problems Solved
1. Enhances the accuracy of timing advance predictions for uplink communications. 2. Enables autonomous updating of timing advance values based on machine learning predictions.
Benefits
1. Improved uplink communication performance. 2. Enhanced network efficiency. 3. Reduced interference in wireless networks.
Potential Commercial Applications
"Wireless Communications Optimization Using Machine Learning Models"
Possible Prior Art
There may be prior art related to using machine learning models for optimizing wireless communication parameters, but specific examples are not provided in the abstract.
Unanswered Questions
How does this technology impact battery life in UEs during uplink communications?
The article does not address the potential impact of using machine learning models on battery consumption in UEs.
What are the potential security implications of autonomous updates of timing advance values based on machine learning predictions?
The abstract does not discuss any security considerations related to autonomous updates of timing advance values in wireless communications.
Original Abstract Submitted
Methods, systems, and devices for wireless communications at a user equipment (UE) are described. A UE may identify one or more measurement parameters to use for input in a machine learning (ML) model. In some cases, the measurement parameters may include reference signal received power measurements or a past set of timing advance (TA) values for a specific node. The UE may predict the TA based on inputting the measurement parameters into the ML model. The UE may transmit an uplink communication from the UE to a network entity using the TA predicted from the ML model. In some examples, the UE may transmit a capability report to indicate that the UE may support autonomous update of the TA based on the predictions of the TA values produced from the ML model.
