Читать книгу Dynamic Spectrum Access Decisions - George F. Elmasry - Страница 49

Receiver operating characteristic (ROC) is not a unique methodology to DSA decision making. It is widely used in many areas where statistical decisions are adaptively made based on myriad metrics. ROC is a generic approach developed for low computational and implementation complexities. This chapter covers different DSA scenarios that rely on using ROC models. A ROC model can be used when probing a frequency band to discover if it is occupied or not at a certain geographical location (e.g., a secondary user is sensing if a primary user is using this frequency band or not). Another scenario that uses a different ROC model is the case of same‐channel in‐band sensing where the ROC model can hypothesize the presence or absence of an interfering signal. The detected energy of the sensed frequency band is compared to an adaptive threshold to hypothesize the presence of the interfering signal. This threshold adaptation is highly dependent on noise estimation and is decided based on tradeoffs driven by the design needs. Estimating ROC models' thresholds can be challenging when noise variance increases and when signal power is too low.¹ The spectrum sensor can be looking into interference from other signals overlaid with the noise without being able to distinguish between the interfering signal power and the noise power. In other cases, the spectrum sensor can be looking at an overlay of different signals occupying the same frequency band. This can mislead the decision‐making process and result in increasing the probability of false alarm and of misdetection.²

With DSA, the ROC methodology can be implemented in different approaches depending on the sensing metrics and where the decision is made. This chapter will start with the generic aspects of the ROC hypothesizing process in DSA applications and present simple ROC‐based decision fusion cases while gradually moving to the harder cases. Statistical decision models in modulation and coding are well studied and well presented in textbooks. This chapter covers decision models for spectrum sensing pointing to the similarities and difference with modulation and coding models. While a demodulator may use fixed thresholds and rely on well‐known statistical models such as AWGN and the communication signal known power spectral density characteristics to decode a symbol, spectrum sensing models use adaptive thresholds and machine learning techniques to account for the many factors that can compound the spectrum sensing hypotheses. If the reader is not familiar with the ROC models, the reader is encouraged to refer to Appendix A of this chapter to get some basic understanding of the ROC methodology.