View source for 17848295. MACHINE LEARNING (ML)-BASED DYNAMIC DEMODULATOR PARAMETER SELECTION simplified abstract (QUALCOMM Incorporated)

=MACHINE LEARNING (ML)-BASED DYNAMIC DEMODULATOR PARAMETER SELECTION=


==Organization Name==

[[:Category:QUALCOMM Incorporated|QUALCOMM Incorporated]]


[[Category:QUALCOMM Incorporated]]

==Inventor(s)==

[[:Category:Jacob Pick of Beit Zayit (IL)|Jacob Pick of Beit Zayit (IL)]][[Category:Jacob Pick of Beit Zayit (IL)]]

[[:Category:Assaf Touboul of Netanya (IL)|Assaf Touboul of Netanya (IL)]][[Category:Assaf Touboul of Netanya (IL)]]

[[:Category:Shay Landis of Hod Hasharon (IL)|Shay Landis of Hod Hasharon (IL)]][[Category:Shay Landis of Hod Hasharon (IL)]]

[[:Category:Alexei Yurievitch Gorokhov of San Diego CA (US)|Alexei Yurievitch Gorokhov of San Diego CA (US)]][[Category:Alexei Yurievitch Gorokhov of San Diego CA (US)]]

[[:Category:Hari Sankar of San Diego CA (US)|Hari Sankar of San Diego CA (US)]][[Category:Hari Sankar of San Diego CA (US)]]

[[:Category:Peer Berger of Hod Hasharon (IL)|Peer Berger of Hod Hasharon (IL)]][[Category:Peer Berger of Hod Hasharon (IL)]]

==MACHINE LEARNING (ML)-BASED DYNAMIC DEMODULATOR PARAMETER SELECTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17848295 titled 'MACHINE LEARNING (ML)-BASED DYNAMIC DEMODULATOR PARAMETER SELECTION

==Simplified Explanation==

The abstract describes a method of wireless communication using an artificial neural network to predict and select the optimal demodulator parameters for each data block, based on the expected features of the data block. This method aims to prevent degradation of demodulation performance while achieving a goal.

* The method uses an artificial neural network to predict the least complex set of demodulator parameters for each data block.
* Multiple sets of demodulator parameters are dynamically selected based on the features of the expected data block.
* The selection process ensures that the chosen set of demodulator parameters does not degrade the demodulation performance compared to a more complex set.

== Potential Applications ==
* Wireless communication systems
* Mobile networks
* Internet of Things (IoT) devices
* Satellite communication

== Problems Solved ==
* Optimizing demodulator parameters for wireless communication
* Preventing degradation of demodulation performance
* Improving overall wireless communication efficiency

== Benefits ==
* Enhanced wireless communication performance
* Efficient utilization of available resources
* Improved reliability and quality of wireless connections
* Adaptability to varying network conditions


==Original Abstract Submitted==

A method of wireless communication by a receiver, includes predicting, with an artificial neural network, at each data block of a set of data blocks, a least complex set of demodulator parameters that will achieve a goal, based on features of a data block expected to be received. The method also includes dynamically selecting the least complex set of demodulator parameters, from multiple sets of demodulator parameters, based on the features of the data block expected to be received. The selecting occurring to prevent degradation of demodulation performance for each data block with the selected set of demodulator parameters for the data block, with respect to a more complex set of demodulator parameters.



[[Category:H04W52/02]]
[[Category:G06N3/02]]