Qualcomm incorporated (20240137884). MACHINE LEARNING BASED TIMING ADVANCE UPDATES simplified abstract

MACHINE LEARNING BASED TIMING ADVANCE UPDATES

Organization Name

qualcomm incorporated

Inventor(s)

Tianyang Bai of Somerville NJ (US)

Hua Wang of Basking Ridge NJ (US)

Yan Zhou of San Diego CA (US)

Junyi Li of Fairless Hills PA (US)

Tao Luo of San Diego CA (US)

MACHINE LEARNING BASED TIMING ADVANCE UPDATES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240137884 titled 'MACHINE LEARNING BASED TIMING ADVANCE UPDATES

Simplified Explanation

The abstract describes methods, systems, and devices for wireless communications at a User Equipment (UE), where the UE utilizes machine learning (ML) models to predict timing advance (TA) values for uplink communications to network entities.

• User Equipment (UE) can identify measurement parameters for input into a machine learning (ML) model.
• Measurement parameters may include reference signal received power measurements or past timing advance (TA) values for a specific node.
• The UE can predict TA values based on inputting the measurement parameters into the ML model.
• The UE can transmit an uplink communication to a network entity using the predicted TA values.
• The UE can transmit a capability report to indicate support for autonomous TA updates based on ML model predictions.

Potential Applications

This technology can be applied in various wireless communication systems to enhance the efficiency and reliability of uplink communications.

Problems Solved

1. Improved accuracy in predicting timing advance values for uplink communications. 2. Enhanced performance of User Equipment (UE) in wireless networks.

Benefits

1. Increased efficiency in uplink communications. 2. Better utilization of resources in wireless networks. 3. Enhanced user experience in wireless communication systems.

Potential Commercial Applications

Optimizing uplink communications in 5G networks for better user experience and network performance.

Possible Prior Art

There may be prior art related to machine learning applications in wireless communications for optimizing network performance and resource allocation.

What are the potential security implications of using machine learning models in wireless communications systems?

Machine learning models in wireless communications systems could potentially be vulnerable to adversarial attacks, leading to security breaches and unauthorized access to network data. It is essential to implement robust security measures to protect ML models from such threats.

How can the accuracy of the predicted timing advance values be further improved in this technology?

The accuracy of predicted timing advance values can be enhanced by incorporating more diverse and relevant measurement parameters into the machine learning model, optimizing the training process, and continuously updating the model with real-time data to adapt to changing network conditions.

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.