Spectrum Sensing using AMC and TFT

Abstract

Spectrum Sensing (SS) is a foremost step to implement next generation Cognitive Radio (CR) systems. The primary goal of a SS technique is to examine whether the Primary User (PU) is in active state or not by analyzing the surrounding radio environment. Traditional methods such as energy detection and Matched Filter Detection (MFD) schemes along with decision making circuits are generally used in SS. However, these techniques are developed under cooperative scenarios and they are used to sense single PU (narrowband sensing). In non-cooperative scenarios and fading channel conditions, traditional techniques produce higher false alarm. If Secondary User (SU) is occupied in the channel then SS task is more difficult. In order to overcome these limitations, a narrowband and wideband SS algorithm using Automatic Modulation Classification (AMC) and Time-Frequency Transform (TFT) is developed in this paper. The performance analysis of proposed AMC and TFT based SS technique under various channel conditions which is also described in this paper.