PRIMARY SIGNAL DETECTION VIA DISTRIBUTED MACHINE LEARNING COORDINATED ACROSS MULTIPLE USER EQUIPMENT DEVICES

Methods and systems for primary signal detection via distributed machine learning coordinated across multiple user equipment devices are disclosed. In an example method, In an example method, a first user equipment (UE) device, located in a predefined area, is caused to determine a first machine lea...

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Bibliographic Details
Main Author Vitebsky, Stanley
Format Patent
LanguageEnglish
Published 02.09.2021
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRICITY
PHYSICS
WIRELESS COMMUNICATIONS NETWORKS
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Summary:Methods and systems for primary signal detection via distributed machine learning coordinated across multiple user equipment devices are disclosed. In an example method, In an example method, a first user equipment (UE) device, located in a predefined area, is caused to determine a first machine learning model configured to detect an anomaly in an RF environment associated with the area. The controller receives the first machine learning model from the first UE device. The controller sends the first machine learning model to a second UE device located in the area. The controller receives anomaly data indicative of an anomaly detected by the second UE device via the first machine learning model. The controller may optionally determine that a primary signal is present in an RF environment associated with the area based on the anomaly data.
Bibliography:Application Number: US202117173384