Sparse Recovery Transmitter Detection

Transmitter detection and separation in radio spec-trum scans is an essential component in emerging spectrum-sharing networks, as it underpins situational awareness for coexistence and enforcement. However, detecting transmitters in noisy real-world traces is challenging and has been tackled with limited practical applicability. Beyond noisy measurements, the challenges stem from the need to simultaneously detect multiple and possibly overlapping transmitter frequency bands and track their transmissions over time. We address these challenges with SCAN (Sparse reCovery trAnsmitter detectioN): an unsupervised approach based on sparse dictionary coding to jointly detect the frequency and temporal behavior of multiple co-occurring transmitters in power spectral density traces. We demonstrate SCAN's applicability to high-noise regimes and across various transmitter co-occurrence scenarios, including when transmitters concurrently overlap in time and frequency (akin to intentional or unintentional inter-ference). We evaluated SCAN's performance with synthetic and real-world traces and in comparison with baselines. We show that SCAN can characterize multiple transmitters even when their power levels are the same. Furthermore, SCAN successfully detects and characterizes 10 simultaneously observed trans-mitters, whereas counterparts fall short even in 3-transmitter scenarios. Finally, we demonstrate that SCAN can discern real-world activity with WiFi, ZigBee, LTE and LoRa transmitters.