BenchLink: An SoC-Based Benchmark for Resilient Communication Links in GPS-Denied Environments

Accurate timing and synchronization, typically enabled by GPS, are essential for modern wireless communication systems. However, many emerging applications must operate in GPS-denied environments where signals are unreliable or disrupted, resulting in oscillator drift and carrier frequency impairments. To address these challenges, we present BenchLink, a System-on-Chip (SoC)-based benchmark for resilient communication links that functions without GPS and supports adaptive pilot density and modulation. Unlike traditional General Purpose Processor (GPP)-based software-defined radios (e.g. USRPs), the SoC-based design allows for more precise latency control. We implement and evaluate BenchLink on Zynq UltraScale+ MPSoCs, and demonstrate its effectiveness in both ground and aerial environments. A comprehensive dataset has also been collected under various conditions. We will make both the SoC-based link design and dataset available to the wireless community. BenchLink is expected to facilitate future research on data-driven link adaptation, resilient synchronization in GPS-denied scenarios, and emerging applications that require precise latency control, such as integrated radar sensing and communication.

MobRFFI: Non-cooperative Device Re-identification for Mobility Intelligence

WiFi-based mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movements. However, MAC address randomization introduces a significant obstacle in accurately estimating congestion levels and path trajectories. To this end, we consider radio frequency fingerprinting and re-identification for attributing WiFi traffic to emitting devices without the use of MAC addresses. We present MobRFFI, an AI-based device fingerprinting and re-identification framework for WiFi networks that leverages an encoder deep learning model to extract unique features based on WiFi chipset hardware impairments. It is entirely independent of frame type. When evaluated on the WiFi fingerprinting dataset WiSig, our approach achieves 94% and 100% device accuracy in multi-day and single-day re-identification scenarios, respectively. We also collect a novel dataset, MobRFFI, for granular multi-receiver WiFi device fingerprinting evaluation. Using the dataset, we demonstrate that the combination of fingerprints from multiple receivers boosts re-identification performance from 81% to 100% on a single-day scenario and from 41% to 100% on a multi-day scenario.