Abstract
Spectrum awareness is an essential aspect of wireless communication technology. Wireless communication systems can obtain spectrum awareness information by monitoring the spectrum usage in the frequency and time domains and representing this information as a time-frequency matrix. In many practical cases it is useful to determine the subsets of elements of such matrix where a signal is present (i.e., the signal area). Several signal area (SA) estimation methods with varying performance have been proposed in the literature. However, there is a lack of comparative research that shows how the configuration of such methods affects their relative performance. In this context, this work investigates the impact of two essential configuration aspects for any SA method, namely the threshold used to decide whether each element of the time/frequency grid contains a signal component or just noise, and the frequency/time resolution of the measurements carried out to obtain such data matrix. Several popular threshold decision criteria and a broad range of measurement resolutions are investigated, showing that these two particular aspects play a key role in the optimum configuration and performance of SA estimation methods. Several useful findings and design guidelines are provided as well.