Improving robustness of cyclostationary detectors to cyclic frequency mismatch using Slepian basis

Abstract

Abstract-Spectrum Sensing (SS) is one of the fundamental mechanisms required by a Cognitive Radio (CR). Among several SS techniques, cyclostationary feature detection is considered as an important technique due to its robustness against noise variance uncertainty and its capability to distinguish among different systems on the basis of their cyclostationary features. However, one of the main limitations of this detector in practical scenarios is its performance degradation in the presence of cyclic frequency mismatch, which mainly arises due to the lack of knowledge about the transmitter clock/oscillator errors at the detector. In this context, this paper proposes a novel solution to address the cyclic frequency mismatch problem utilizing the Slepian basis expansion instead of the widely used Fourier basis expansion. It is shown that the proposed approach captures the deviation in the cyclic frequency caused by the aforementioned imperfections and hence provides a significant improvement in the sensing performance in the presence of cyclic frequency mismatch.

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

“…The state ℋ0 corresponds primary user absence and state ℋ1 corresponds primary user presence. For the sensing decision, several of the previously mentioned spectrum sensing techniques can be used, including energy detection [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], cyclostationary detection [21,22,23,24,25,26,27], matched filter detection [28,29,30,31], covariance-based detection [32,33,34,35,36,37,38,39], and machine-learning based detection [40,41,42,43,44,45,46,47,48,49,50,51] which are discussed below. These techniques are often evaluated using the probabilities of false alarm and probability of detection.…”

“…Cyclostationary feature detection [21,22,23,24,25,26,27] relies on certain received signal features. Some statistics of the transmitted signal, such as modulation rate and carrier frequency, are periodic and perceived as cyclostationary features.…”

“…As for the previously mentioned techniques, the use of a static threshold can lead to less accurate results because of the noise uncertainty. This issue has motivated the authors of [25] to investigate the use of a dynamic selection of the threshold to enhance the detection performance of this sensing technique. Matched filter techniques are optimal in the sense that they require only a few samples to achieve good performance detection, but they are not very practical, as prior information about the primary user signal is not always available.…”

Multi‐cycle spectrum sensing for OFDM signals under cyclic frequency offsets in cognitive vehicular networks

“…The knowledge‐aided methods require either a full or partial information about the signal of the PU and/or about its position. In particular, among these methods, we have to mention that one proposed in [9], which estimates the cyclic autocorrelation function (CAF) exploiting the Slepian basis expansion, instead of the conventional Fourier basis expansion, aiming at increasing the robustness to the CFO. However, this approach requires a priori knowledge of the CFO to provide an optimal solution.…”

Blind Detection Techniques for Non-Cooperative Communication Signals Based on Deep Learning

“…However, this method has a poor detection performance in low signal-tonoise (SNR) environments. A more advanced method, cyclostationary feature detection (CFD) [9]- [11]…”