Gavin, B. orcid.org/0009-0009-8694-2288, Deng, T. orcid.org/0000-0003-4507-5746 and Ball, E. orcid.org/0000-0002-6283-5949 (2024) Low area and low power FPGA implementation of a DBSCAN-based RF modulation classifier. IEEE Open Journal of the Computer Society, 5. 50 -61. ISSN 2644-1268
Abstract
This paper presents a new low-area and low-power Field Programmable Gate Array (FPGA) implementation of a Radio Frequency (RF) modulation classifier based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, known as DBCLASS. The proposed architecture demonstrates a novel approach for the efficient hardware realisation of the DBSCAN algorithm by utilising parallelism, a bespoke sorting algorithm, and eliminating memory access. The design achieves 100% classification accuracy with labcaptured RF data above 8 dB signal-to-noise ratio(SNR) whilst exhibiting an improvement of latency in comparison to the next quickest design by a factor of 7.5, a reduction in terms of total FPGA resources used in comparison to the next smallest complete system by a factor of 3.65, and a reduction in power consumption over the next most efficient by a factor of 4.75. The proposed design is well suited for resource-constrained applications, such as mobile cognitive radios and spectrum monitoring systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Keywords: | RF Classifier; Cognitive Radio; DBSCAN; FPGA; Automatic Modulation Classification; AMC; Beyond Smart Radio |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Feb 2024 14:21 |
Last Modified: | 07 Nov 2024 15:54 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/ojcs.2024.3355693 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208739 |
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