Advanced wireless communication techniques, like those found in 5G and beyond, require low latency while operating on high throughput streams of radio frequency (RF) data. Automatic Modulation Classification is one important method to understand how other radios are using the wireless channel. This information can be used in applications such as cognitive radios to better utilize the wireless channel and transmit information at faster rates.
Our recent work shows how to perform modulation classification in real-time by exploiting the RF capabilities offered by Xilinx RFSoC platforms. This work, lead by the University of Sydney Computer Engineering Lab, developed a non-uniform and layer-wise quantization technique to shrink the large memory footprint of neural networks to fit on the FPGA fabric. This technique preserves the classification accuracy in a real-time implementation.
This work was published at the Reconfigurable Architectures Workshop (RAW) and an open source implementation on Xilinx RFSoC ZCU111 development board is available at in the github repo.