Spectral Sensing Performance for Feature-Based Signal Detection with Imperfect Training

Abstract

In this chapter, the effect of imperfect training data on feature-based signal detection is explored, as it relates to both training time and detection performance in a cognitive radio system. The improved performance of feature-based detection comes at the cost of either having to know in advance the signal features present in primary user transmissions (an unrealistic assumption) or learning them whilst operating “in the field.” Such learning, however, necessarily takes place with signal sets which do not perfectly represent the features of the primary users' modulated signals. Using a two-stage detector performing both feature training and sensing functions, it is shown in this chapter that reducing the learning time generally results in poorer detection performance and vice-versa. A suitable trade-off between these two outcomes is obtained by optimizing a cost function that takes both factors into consideration. Cyclostationarity detection is specifically considered.