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1. (WO2018204917) SPECTRAL SENSING AND ALLOCATION USING DEEP MACHINE LEARNING

Pub. No.:    WO/2018/204917    International Application No.:    PCT/US2018/031395
Publication Date: Fri Nov 09 00:59:59 CET 2018 International Filing Date: Tue May 08 01:59:59 CEST 2018
IPC: H04K 3/00
H04W 24/00
H04W 72/08
H04W 88/02
Applicants: BALL AEROSPACE & TECHNOLOGIES CORP.
Inventors: SHIMA, James, Michael
Title: SPECTRAL SENSING AND ALLOCATION USING DEEP MACHINE LEARNING
Abstract:
Methods and systems for identifying occupied areas of a radio frequency (RF) spectrum, identifying areas within that RF spectrum that are unusable for further transmissions, and identifying areas within that RF spectrum that are occupied but that may nonetheless be available for additional RF transmissions are provided. Implementation of the method then systems can include the use of multiple deep neural networks (DNNs), such as convolutional neural networks (CNN's), that are provided with inputs in the form of RF spectrograms. Embodiments of the present disclosure can be applied to cognitive radios or other configurable communication devices, including but not limited to multiple inputs multiple output (MIMO) devices and 5G communication system devices.