The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 26, 2022

Filed:

Jul. 01, 2020
Applicant:

Celona, Inc., Cupertino, CA (US);

Inventors:

Nagi Mahalingam, San Diego, CA (US);

Mark Jan Dijkstra, Brooklyn, NY (US);

Sourav Bandyopadhyay, Liluah Howrah, IN;

Assignee:

Celona, Inc., Cupertino, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
H04W 16/14 (2009.01); H04W 72/04 (2009.01); H04W 88/08 (2009.01); H04W 4/021 (2018.01); G06N 20/00 (2019.01); H04W 48/16 (2009.01); H04W 28/08 (2009.01);
U.S. Cl.
CPC ...
H04W 16/14 (2013.01); G06N 20/00 (2019.01); H04W 4/022 (2013.01); H04W 28/0942 (2020.05); H04W 48/16 (2013.01); H04W 72/0453 (2013.01); H04W 88/08 (2013.01);
Abstract

A method and apparatus for predicting capacity utilization and preemptively reallocating channels in a spectrum-controlled network such as a Citizen's Band Radio Service (CBRS) network. The network includes a plurality of Base Stations/Access Points (BS/APs) which monitor RF resource usage of the channels at each BS/AP and provide time series data relating to capacity utilization over a period of time. The data is analyzed, and patterns are identified in the collected capacity utilization data using Artificial Intelligence (AI) techniques. Looking forward, capacity utilization is predicted using AI techniques. Responsive to the capacity utilization predictions, the channels pre-emptively reallocated among BS/APs. The enterprise network provides feedback regarding prediction accuracy, which is utilized in a machine learning process to modify the models and provide greater prediction accuracy. The prediction apparatus includes a capacity utilization module and a training/retraining module, which may be located remotely from the enterprise network.


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