Spectrum Activity Surveillance: Modeling and Analysis From Perspectives of Surveillance Coverage and Culprit Detection

Spectrum activity surveillance (SAS) is essential to dynamic spectrum access (DSA)-enabled systems with a two-fold impact: it is a primitive mechanism to collect usage data for spectrum efficiency improvement; it is also a prime widget to collect misuse forensics of unauthorized or malicious users. While realizing SAS for DSA-enabled systems appears to be intuitive and trivial, it is, however, a challenging yet open problem. On one hand, a large-scale SAS function is costly to implement in practice; on the other hand, it is not clear how to characterize the efficacy and performance of monitor deployment strategies. To address such challenges, we introduce a three-factor space, composed of spectrum, time, and geographic region, over which the SAS problem is formulated by a two-step solution: 3D-tessellation for sweep (monitoring) coverage and graph walk for detecting spectrum culprits, that is, devices responsible for unauthorized spectrum occupancy. In particular, our system model transforms SAS from a globally collective activity to localized actions, and strategy objectives from qualitative attributes to quantitative measures. With this model, we design low-cost deterministic strategies for dedicated monitors, which outperform strategies found by genetic algorithms, and performance-guaranteed random strategies for crowd-source monitors, which can detect adversarial spectrum culprits in bounded time.

Automated summary

Spectrum activity surveillance is crucial for DSA systems, improving spectrum efficiency and detecting malicious users, but implementing it is challenging. To address these challenges, a three-factor space is introduced, with a two-step solution proposed to tackle the SAS problem.