A framework to analyze decision strategies for multi-band spectrum sensing in cognitive radios
Spectrum sensing is an essential aspect of cognitive radios that allows secondary users (SUs) to opportunistically use licensed frequency bands while the primary user is idle. In this paper, we assume the available spectrum is divided into multiple bands, and SUs search over these bands to detect the empty bands, which is known as multi-band spectrum sensing. Most of the recent work focus on spectrum sensing in a given band which can be modeled as a binary hypothesis problem and has performance metrics such as receiver operating characteristics (ROC) curves. However, this framework is not appropriate for multi-band spectrum sensing problem, which can be modeled as a multi-hypothesis testing problem. The contribution of this paper is three-fold: (i) a novel approach to evaluate the sensing performance of the multi-band spectrum-sensing using localization ROCs; (ii) optimal spectrum-sensing strategies that maximize the area under the localization ROC curves that can be used as performance benchmarks for any other multi-band spectrum sensing technique; (iii) a framework to analyze the energy expenditure of multi-band spectrum sensing.
Cognitive radios, Energy efficiency, Localized ROC curves, Multi-band spectrum sensing
Jay B. Patel, Steven Collins, and Birsen Sirkeci-Mergen. "A framework to analyze decision strategies for multi-band spectrum sensing in cognitive radios" Physical Communication (2020). https://doi.org/10.1016/j.phycom.2020.101139