View source for LEVL PARENT, LLC (20240275441). SYSTEM AND METHOD FOR RADIO FREQUENCY FINGERPRINTING simplified abstract

=SYSTEM AND METHOD FOR RADIO FREQUENCY FINGERPRINTING=


==Organization Name==

[[:Category:LEVL PARENT, LLC|LEVL PARENT, LLC]]


[[Category:LEVL PARENT, LLC]]

==Inventor(s)==

[[:Category:Noam Janco of Tel Aviv (IL)|Noam Janco of Tel Aviv (IL)]][[Category:Noam Janco of Tel Aviv (IL)]]

[[:Category:Nuriel Rogel of Rehovot (IL)|Nuriel Rogel of Rehovot (IL)]][[Category:Nuriel Rogel of Rehovot (IL)]]

==SYSTEM AND METHOD FOR RADIO FREQUENCY FINGERPRINTING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240275441 titled 'SYSTEM AND METHOD FOR RADIO FREQUENCY FINGERPRINTING

The abstract of this patent application describes a computer-implemented method that involves monitoring wireless channel estimations between beamformer communication devices and beamformee communication devices, training a machine learning model on a dataset of standard beamforming protocol dataframes, and applying the trained model to predict the origin of a target transmission.

* Monitoring wireless channel estimations between beamformer and beamformee communication devices
* Training a machine learning model on a dataset of standard beamforming protocol dataframes and labels
* Applying the trained model to predict the origin of a target transmission

Potential Applications:
- Wireless communication systems
- Beamforming technology
- Machine learning in wireless networks

Problems Solved:
- Improving the accuracy of identifying the source of wireless transmissions
- Enhancing communication efficiency in beamforming systems

Benefits:
- Increased reliability in wireless communication
- Optimized use of beamforming technology
- Enhanced performance of machine learning models in wireless networks

Commercial Applications:
Title: "Enhancing Wireless Communication Efficiency with Machine Learning"
This technology can be utilized in telecommunications, IoT devices, and smart home systems to improve data transmission and reception.

Questions about the technology:
1. How does this method improve the efficiency of wireless communication systems?
- By utilizing machine learning to predict the origin of wireless transmissions, this method enhances the accuracy and reliability of communication channels.
2. What are the potential implications of integrating this technology into existing beamforming systems?
- By incorporating machine learning models, beamforming systems can achieve higher performance and optimize data transmission processes.


==Original Abstract Submitted==

a computer-implemented method comprising: monitoring transmissions representing estimation of a wireless channel, between at least one beamformer communication device and a plurality of beamformee communication devices; at a training stage, training a machine learning model on a training dataset comprising: (i) a plurality dataframes of a standard beamforming protocol associated with at least some of the monitored transmissions, and (ii) labels indicating an association between the dataframes and the stas; and at an inference stage, applying the trained machine learning model to a target transmission representing estimation of a wireless channel, to predict whether the target transmission was transmitted from one of the stas.



[[Category:H04B7/0456]]
[[Category:H04B7/0417]]
[[Category:CPC_H04B7/0478]]