On Oversampling-Based Signal Detection
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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.
KeywordsCognitive radio Colored noise Detection Internet-of-Things Noise uncertainty Oversampling Wideband spectrum sensing
This work was supported in part by MIUR under the program “Dipartimenti di Eccellenza (2018–2022)—Precise-CPS,” and in part by the EU project eCircular (EIT Climate-KIC). The material in this paper was presented in part at the IEEE Int. Symp. on Personal, Indoor and Mobile Radio Comm. (PIMRC 2018), Bologna, Italy, Sep. 2018.
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