Abstract
Cyclostationary Feature Detection Based Blind Approach for Spectrum Sensing and Classification
Highlights
The advances in wireless communication and emerging wireless multimedia applications have led to a huge demand for radio spectrum
Cyclostationary Feature Detection (CFD), using a crest factor developed from the cyclic spectrum and a threshold formed using the noise information that is obtained in the absence of primary users (PU), is applied for SS [14]
In order to understand the performance of the algorithms, the probability of detection was calculated for different modulation schemes with varying SNR
Summary
The advances in wireless communication and emerging wireless multimedia applications have led to a huge demand for radio spectrum. CR is a promising solution provided by the concept of dynamic spectrum access, where the users intelligently sense the spectrum and access the vacant bands [2] It is an intelligent radio and network technology that can detect the available spectrum bands and adjust its transmission parameters . Cyclostationary Feature Detection (CFD) makes use of the periodicity in auto-correlation of the modulated signals. The detection has been expanded to multiple SUs by making use of cooperative sensing, but assumes the cyclic frequencies are known apriori. 1, APRIL 2019 has been formulated to identify the modulation scheme of the signal received without making use of Neural networks.
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