T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge

Abstract--Spectrum sharing allows different protocols of the same standard (e.g., 802.11 family) or different standards (e.g., LTE and DVB) to coexist in overlapping frequency bands. Third, it utilizes an extensive 66 GB dataset of over-the-air (OTA) WiFi transmissions for training, which is released along with the code for community use. Legacy protocol detection methods are integrated into the RF receiver network interface card, enabling fast preamble I. However, updating the system for new protocols leads to backward compatibility issues and The increasing demand for wireless services has caused a potential detection errors in challenging wireless channels. This results in congested the other hand, software-defined radio (SDR) systems offer wireless spectrum environments as various communication flexibility but introduce higher latencies due to data transfer protocols coexist in the same frequency bands [2]. This paper transmissions further raise security concerns, posing demonstrates that using edge devices with CPU and GPU risks to critical operations [3]. Detecting diverse protocols in on a system-on-a-module (SOM), along with careful software crowded spectrums allows for intelligent strategies to mitigate design, can overcome the these limitations and enable realtime interference, improve spectral efficiency, and enhance overall processing for complex ML wireless applications on an wireless system performance, benefiting regulatory bodies, SDR-based edge device.