Classification of LoRa Signals With Real-Time Validation Using the Xilinx Radio Frequency System-on-Chip

Abstract

This paper demonstrates a real-time LoRa Internet of Things (IoT) signal classification technique that runs on Xilinx Radio Frequency System-on-Chip (RFSoC) hardware. IoT signals are being used for wider arrays of applications and therefore awareness of their presence is important for cyber security and infrastructure protection as well as battlefield situational awareness. Within this research a dataset of LoRa waveforms is captured using the RFSoC which bounds the possible combinations of waveform parameters. Offline algorithms are tested against this data to evaluate how to extract the centre frequency, bandwidth and spreading factor. The algorithms are then adapted to run natively on the Xilinx RFSoC itself to enable real-time classification of waveforms from non-cooperative LoRa transmitters with a high degree of classification success.

Modular Multi-Channel RFSoC System Expansion and Array Design

“…Previous studies have used ARESTOR for performing a diverse range of experimental campaigns including multiband active radar measurements of drones [5], simultaneous active and passive radar measurements of human targets [6], integrated sensing and communications (ISAC) [7], [8] and real-time detection and classification of internet of things (IoT) signals [9].…”