18548859. SOUNDING AND TRANSMISSION PRECODING MATRIX INDICATION DETERMINATION USING MACHINE LEARNING MODELS simplified abstract (QUALCOMM Incorporated)
Contents
- 1 SOUNDING AND TRANSMISSION PRECODING MATRIX INDICATION DETERMINATION USING MACHINE LEARNING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SOUNDING AND TRANSMISSION PRECODING MATRIX INDICATION DETERMINATION USING MACHINE LEARNING MODELS - 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
SOUNDING AND TRANSMISSION PRECODING MATRIX INDICATION DETERMINATION USING MACHINE LEARNING MODELS
Organization Name
Inventor(s)
SOUNDING AND TRANSMISSION PRECODING MATRIX INDICATION DETERMINATION USING MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18548859 titled 'SOUNDING AND TRANSMISSION PRECODING MATRIX INDICATION DETERMINATION USING MACHINE LEARNING MODELS
Simplified Explanation
The present disclosure provides techniques for using machine learning models to sound and precoding uplink transmissions in a wireless network.
- Generating a sounding reference signal (SRS) using a deep neural network (DNN)
- Transmitting the generated SRS to a network entity
- Receiving information on a precoding matrix for uplink transmissions
- Precoding uplink transmissions based on the identified matrix
- Transmitting the precoded uplink transmissions on a shared channel
Potential Applications
This technology can be applied in various wireless communication systems to improve the efficiency and reliability of uplink transmissions.
Problems Solved
1. Enhancing the accuracy of sounding and precoding techniques in wireless networks 2. Optimizing uplink transmissions on shared channels for better performance
Benefits
1. Improved signal quality and reliability in uplink transmissions 2. Enhanced network efficiency and capacity 3. Automated optimization through machine learning models
Potential Commercial Applications
Optimizing uplink transmissions in 5G networks for better performance and user experience
Possible Prior Art
Prior art may include traditional methods of sounding and precoding in wireless networks, which may not be as efficient or accurate as the techniques described in this disclosure.
Unanswered Questions
How does this technology impact network latency in real-world deployments?
This article does not address the specific impact of this technology on network latency, which is crucial for applications requiring low latency communication such as real-time gaming or industrial automation.
What are the potential security implications of using machine learning models for wireless transmission optimization?
The article does not delve into the potential security vulnerabilities or risks associated with implementing machine learning models for optimizing wireless transmissions, which could be a concern for network operators and users.
Original Abstract Submitted
Certain aspects of the present disclosure provide techniques for sounding and precoding uplink transmissions using one or more machine learning models. An example method generally includes generating a sounding reference signal (SRS) using an SRS deep neural network (DNN); transmitting, to a network entity, the generated SRS in one or more resource elements (REs); receiving, from the network entity, information identifying a precoding matrix to use for uplink transmissions on a shared channel; precoding uplink transmissions based on the identified precoding matrix; and transmitting, to the network entity, the precoded uplink transmissions on the shared channel.
