Correlation-Based Sensing for Cognitive Radio Networks: Bounds and Experimental Assessment
Rajesh Kumar Sharma, Jon W. Wallace · IEEE Sensors Journal · 2010
Minimal missed detection rate of primary users is critical for adoption of cognitive radio networks, underlining the need for robust collaborative sensing combined with near-optimal single-node detection methods. Although correlation-based detection methods potentially provide needed per-node performance improvements for correlated signals, their performance for realistic blind sensing is unclear since the type and extent of correlation may be unknown in practice. Although standard Neymon–Pearson (NP) based detection can be applied when correlation is perfectly known, difficulty arises when the correlation is random, which is the focus of this paper. A tighter bound for the performance of correlation-based methods is developed herein based on a signal with random correlation and NP detection under the assumption of correlation distribution information (CDI). Simulations of existing ad-hoc correlation-based detectors are compared to the upperbound using a simple uniform random correlation model (RCM). Additionally, a measurement campaign is presented where radio-frequency (RF) spectra in many bands of interest are measured throughout a large sub-urban environment, generating realistic models for the random signal correlation. The measurement-based model indicates limits on performance gains possible with correlation-based detection and how well existing ad-hoc techniques can be expected to perform in practice.