18162394. BEAMFORMING ENHANCEMENTS USING MACHINE LEARNING MODELS simplified abstract (QUALCOMM Incorporated)

BEAMFORMING ENHANCEMENTS USING MACHINE LEARNING MODELS

Organization Name

QUALCOMM Incorporated

Inventor(s)

Gaurang Naik of San Diego CA (US)

Abhishek Pramod Patil of San Diego CA (US)

George Cherian of San Diego CA (US)

Yanjun Sun of San Diego CA (US)

Sai Yiu Duncan Ho of San Diego CA (US)

Alfred Asterjadhi of San Diego CA (US)

Abdel Karim Ajami of Lakeside CA (US)

Lin Yang of San Diego CA (US)

BEAMFORMING ENHANCEMENTS USING MACHINE LEARNING MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18162394 titled 'BEAMFORMING ENHANCEMENTS USING MACHINE LEARNING MODELS

Simplified Explanation

The abstract describes methods, systems, and devices for wireless communications, specifically focusing on beamforming procedures between two wireless communication devices. The first device predicts future parameters for communication based on a machine learning model and selects a beamforming configuration accordingly.

• Wireless communication devices can determine parameters for communication within specific time intervals.
• Machine learning models can be used to predict future parameters for communication.
• Beamforming configurations can be selected based on predicted parameters for optimized communication.

Potential Applications

This technology can be applied in various industries such as telecommunications, IoT, and smart devices to improve wireless communication efficiency.

Problems Solved

This technology solves the problem of optimizing wireless communication between devices by predicting future parameters and selecting the best beamforming configuration.

Benefits

The benefits of this technology include improved communication reliability, reduced interference, and increased data transfer speeds between wireless devices.

Potential Commercial Applications

Potential commercial applications of this technology include 5G networks, smart homes, industrial IoT, and autonomous vehicles.

Possible Prior Art

One possible prior art for this technology could be research papers or patents related to machine learning in wireless communication systems.

What are the specific machine learning models used for predicting future parameters in wireless communication systems?

The article does not specify the exact machine learning models used for predicting future parameters in wireless communication systems.

How does this technology compare to traditional beamforming techniques in terms of performance and efficiency?

The article does not provide a direct comparison between this technology and traditional beamforming techniques in terms of performance and efficiency.

Original Abstract Submitted

Methods, systems, and devices for wireless communications are described. A first wireless communication device may determine a first set of parameters for communicating with a second wireless communication device within a first set of time intervals, the first set of parameters associated with a beamforming procedure. The first wireless communication device may predict, based on inputting the first set of parameters to a machine learning model, a second set of parameters corresponding to a second set of time intervals in the future after the first set of time intervals. The first wireless communication device may select a beamforming configuration for communications between the first wireless communication device and the second wireless communication device based on the second set of parameters, and may communicate with the second wireless communication device within the second set of time intervals in accordance with the beamforming configuration.