RFSoC modulation classification with streaming CNN : data set generation & quantized-aware training

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MacLellan, Andrew and Crockett, Louise and Stewart, Robert (2024) RFSoC modulation classification with streaming CNN : data set generation & quantized-aware training. IEEE Open Journal of Circuits and Systems. ISSN 2644-1225 (https://doi.org/10.1109/OJCAS.2024.3509627)

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Abstract

This paper introduces a novel FPGA-based Convolutional Neural Network (CNN) architecture for continuous radio data processing, specifically targeting modulation classification on the Zynq UltraScale+ Radio Frequency System on Chip (RFSoC) operating in real-time. Evaluated on AMD’s RFSoC2x2 development board, the design integrates General Matrix Multiplication (GEMM) optimisations and fixed-point arithmetic. We also present a method for creating Deep Learning (DL) data sets for wireless communications, incorporating the RFSoC into the data generation loop. Furthermore, we explore quantised-aware training, producing three modulation classification models with different fixed-point weight precisions (16-bit, 8-bit, and 4-bit). We interface with the implemented hardware through the open-source PYNQ project, which combines Python with programmable logic interaction, enabling real-time modulation prediction via a PYNQ-enabled Jupyter app. The three models, operating at a 128 MHz sampling rate prior to the decimation stage, were evaluated for accuracy and resource consumption. The 16-bit model achieved the highest accuracy with minimal additional resource usage compared to the 8-bit and 4-bit models, making it the optimal choice for deploying a modulation classifier at the receiver.

ORCID iDs

MacLellan, Andrew ORCID logoORCID: https://orcid.org/0000-0001-9624-2212, Crockett, Louise ORCID logoORCID: https://orcid.org/0000-0003-4436-0254 and Stewart, Robert ORCID logoORCID: https://orcid.org/0000-0002-7779-8597;
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