GRCon20 - Deep learning inference in GNU Radio with ONNX

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This paper introduces gr-dnn, an open source GNU Radio Out Of Tree (OOT) block capable of running deep learning inference inside GNU Radio flow graphs. This module integrates a deep learning inference engine from the Open Neural Network Exchange (ONNX) project. Thanks to the interoperability with most of the major deep learning frameworks, it does not impose any restriction on the tool used by the model designer.

As an example, we demonstrate here its functionalities running a simple deep learning inference model on raw radio samples acquired with a PlutoSDR.

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It looks like you are smack-bang in the 802.11 wifi spectrum, but you determine it to be PAM4, and not QAM265 or something less?

notsure

Interesting because I’m made aware of yet another area, having apparently important information, of which I have no idea what you’re talking about. Sadly this program is not devised for the public.

slatch

GRCon20 - Deep learning inference in GNU Radio with ONNX

GRCon20 - Deep learning inference in GNU Radio with ONNX

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