FPGA accelerated deep learning radio modulation classification using MATLAB system objects & PYNQ

Tools

Maclellan, Andrew and McLaughlin, Lewis and Crockett, Louise and Stewart, Robert W. (2019) FPGA accelerated deep learning radio modulation classification using MATLAB system objects & PYNQ. In: 29th International Conference on Field-Programmable Logic and Applications, 2019-09-09 - 2019-09-11, Barcelona Supercomputing Center and Universitat Politècnica de Catalunya.

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Abstract

Deep learning (DL) and Artificial Intelligence (AI) have proven to be exciting and powerful machine learning-based techniques that have solved many real world challenges. They have made their mark in the image and video processing and natural language processing fields and now seek to make an impact on radio communications. With the increasing demand of high quality wireless data processing for spectrum sensing; cognitive radio; and accurate channel estimation, DL techniques could be used as the new state of the art answers to these problems.

ORCID iDs

Maclellan, Andrew ORCID logoORCID: https://orcid.org/0000-0001-9624-2212, McLaughlin, Lewis, Crockett, Louise ORCID logoORCID: https://orcid.org/0000-0003-4436-0254 and Stewart, Robert W. ORCID logoORCID: https://orcid.org/0000-0002-7779-8597;
CORE (COnnecting REpositories)