# On Oversampling-Based Signal Detection

## TL;DR

This paper investigates robust wideband spectrum sensing in oversampled cognitive radio systems, addressing front-end impairments and noise uncertainties, and proposes new frequency-domain detectors that outperform existing methods.

## Contribution

It introduces novel frequency-domain detectors designed to be robust against noise uncertainty and front-end impairments in oversampled spectrum sensing.

## Key findings

- Noise-uncertainty immune energy detector performs best.
- Detectors matching the receiver noise PSD outperform others.
- Proposed detectors outperform existing spectrum sensing techniques.

## Abstract

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.