Qualcomm incorporated (20240163136). SOUNDING AND TRANSMISSION PRECODING MATRIX INDICATION DETERMINATION USING MACHINE LEARNING MODELS simplified abstract
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 20240163136 titled 'SOUNDING AND TRANSMISSION PRECODING MATRIX INDICATION DETERMINATION USING MACHINE LEARNING MODELS
Simplified Explanation
The present disclosure describes techniques for using machine learning models to improve uplink transmissions in a 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 precoding matrix
- Transmitting the precoded uplink transmissions on a shared channel
Potential Applications
This technology could be applied in wireless communication systems to enhance the efficiency and reliability of uplink transmissions.
Problems Solved
This technology helps in optimizing uplink transmissions by utilizing machine learning models to improve precoding techniques.
Benefits
The use of machine learning models can lead to more accurate precoding of uplink transmissions, resulting in better overall network performance.
Potential Commercial Applications
This technology could be valuable for telecommunications companies looking to improve the quality of their network services through advanced signal processing techniques.
Possible Prior Art
Prior art in this field may include traditional methods of precoding uplink transmissions without the use of machine learning models.
Unanswered Questions
How does this technology compare to existing precoding techniques in terms of performance and efficiency?
This article does not provide a direct comparison between this technology and traditional precoding methods.
What are the potential limitations or challenges in implementing this technology in real-world networks?
The article does not address any potential obstacles or difficulties that may arise when deploying this technology in practical network environments.
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.
