RFSoC Modulation Classification with Streaming CNN: Data Set Generation & Quantized-Aware Training

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.