  The availability of inexpensive devices allows nowadays to implement
cognitive radio functionalities in large-scale networks such as the
internet-of-things and future mobile cellular systems. In this paper, we focus
on wideband spectrum sensing in the presence of oversampling, i.e., the
sampling frequency of a digital receiver is larger than the signal bandwidth,
where signal detection must take into account the front-end impairments of
low-cost devices. Based on the noise model of a software-defined radio dongle,
we address the problem of robust signal detection in the presence of noise
power uncertainty and non-flat noise power spectral density (PSD). In
particular, we analyze the receiver operating characteristic of several
detectors in the presence of such front-end impairments, to assess the
performance attainable in a real-world scenario. We propose new
frequency-domain detectors, some of which are proven to outperform previously
proposed spectrum sensing techniques such as, e.g., eigenvalue-based tests. The
study shows that the best performance is provided by a noise-uncertainty immune
energy detector (ED) and, for the colored noise case, by tests that match the
PSD of the receiver noise.
