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

Andrew Maclellan; Lewis McLaughlin; Louise  Crockett; Robert W. Stewart

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

Andrew Maclellan, Lewis McLaughlin, Louise Crockett, Robert W. Stewart

Electronic And Electrical Engineering

Research output: Contribution to conference › Poster › peer-review

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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.

Original language	English
Number of pages	1
Publication status	Published - 10 Sept 2019
Event	29th International Conference on Field-Programmable Logic and Applications - Barcelona Supercomputing Center and Universitat Politècnica de Catalunya, Barcelona, Spain Duration: 9 Sept 2019 → 11 Sept 2019

Conference

Conference	29th International Conference on Field-Programmable Logic and Applications
Abbreviated title	FPL 2019
Country/Territory	Spain
City	Barcelona
Period	9/09/19 → 11/09/19

Keywords

deep learning
machine learning
radio
communications

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Maclellan-etal-FPL-2019-Poster-FPGA-accelerated-deep-learning-radio-modulation-classificationAccepted author manuscript, 1.12 MB

Cite this

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T1 - FPGA accelerated deep learning radio modulation classification using MATLAB system objects & PYNQ

AU - Maclellan, Andrew

AU - McLaughlin, Lewis

AU - Crockett, Louise

AU - Stewart, Robert W.

PY - 2019/9/10

Y1 - 2019/9/10

N2 - 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.

AB - 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.

KW - deep learning

KW - machine learning

KW - radio

KW - communications

UR - https://fpl2019.bsc.es/

M3 - Poster

T2 - 29th International Conference on Field-Programmable Logic and Applications

Y2 - 9 September 2019 through 11 September 2019

ER -

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

Abstract

Conference

Keywords

Access to Document

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

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