Qualcomm incorporated (20240163686). BEAMFORMING ENHANCEMENTS USING MACHINE LEARNING MODELS simplified abstract

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 20240163686 titled 'BEAMFORMING ENHANCEMENTS USING MACHINE LEARNING MODELS

Simplified Explanation

The abstract describes methods, systems, and devices for wireless communications involving beamforming procedures. A wireless communication device determines parameters for communication with another device and predicts future parameters using a machine learning model. The device selects a beamforming configuration based on the predicted parameters and communicates accordingly.

• Wireless communication device determines parameters for communication with another device
• Machine learning model predicts future parameters based on input parameters
• Device selects beamforming configuration based on predicted parameters
• Communication with another device occurs based on selected beamforming configuration

Potential Applications

This technology can be applied in various wireless communication systems, such as 5G networks, IoT devices, and smart city infrastructure.

Problems Solved

This technology helps optimize wireless communication by predicting future parameters and selecting appropriate beamforming configurations, leading to improved signal quality and network efficiency.

Benefits

The benefits of this technology include enhanced communication reliability, increased data transfer speeds, reduced interference, and overall improved performance of wireless networks.

Potential Commercial Applications

Potential commercial applications of this technology include telecommunications companies, network equipment manufacturers, IoT device manufacturers, and smart city infrastructure developers.

Possible Prior Art

One possible prior art in this field is the use of machine learning models for optimizing wireless communication systems, but the specific application of predicting future parameters for beamforming configurations may be a novel aspect of this technology.

How does this technology impact battery life in wireless devices?

This article does not address the potential impact of this technology on the battery life of wireless devices. Implementing beamforming configurations based on predicted parameters may require additional computational resources, which could affect battery consumption. Further research is needed to understand the trade-offs between improved communication performance and battery life in wireless devices.

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

The article does not discuss the security implications of using machine learning models for predicting parameters in wireless communication systems. It is essential to investigate potential vulnerabilities and risks associated with machine learning algorithms in this context, such as data privacy concerns, adversarial attacks, and model robustness against malicious actors. Further research is needed to address these security challenges and ensure the safe and secure operation of wireless communication systems.

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.