8 research outputs found
Two-Stage Detectors with Multiple Energy Detectors and Adaptive Double Threshold in Cognitive Radio Networks
Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radioâbased industrial wireless sensor networks
Modern energy detectors typically use adaptive threshold estimation algorithms to improve signal detection in cognitive radioâbased industrial wireless sensor networks (CRâIWSNs). However, a number of adaptive threshold estimation algorithms often perform poorly under noise uncertainty conditions since they are typically unable to autoâadapt their parameter values per changing spectra conditions. Consequently, in this paper, we have developed two new algorithms to accurately and autonomously estimate threshold values in CRâIWSNs under dynamic spectra conditions. The first algorithm is a parametricâbased technique termed the histogram partitioning algorithm, whereas the second algorithm is a fully autonomous variant termed the meanâbased histogram partitioning algorithm. We have evaluated and compared both algorithms with some wellâknown methods under different CR sensing conditions. Our findings indicate that both algorithms maintained over 90% probability of detection in both narrow and wideband sensing conditions and less than 10% probability of false alarm under noiseâonly conditions. Both algorithms are quick and highly scalable with a time complexity of O(V), where V is the total number of input samples. The simplicity, effectiveness, and viability of both algorithms make them typically suited for use in CRâIWSN applications.http://wileyonlinelibrary.com/journal/ett2020-10-01hj2019Electrical, Electronic and Computer Engineerin