Energy detection based spectrum sensing over η-λ-μ fading channel

2016 ◽  
Author(s):  
Kamal M. Captain ◽  
Manjunath V. Joshi
Keyword(s):  
Spectrum Sensing ◽  
Fading Channel ◽  
Energy Detection

scholarly journals Analysis of Cognitive Radio Spectrum Sensing Techniques over Nakagami-m Fading Channel

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Fading Channel ◽  
Feature Detection ◽  
Energy Detection ◽  
Radio Spectrum ◽  
Awgn Channel ◽  
Novel Approach ◽  
Spectrum Scarcity ◽  
Cyclostationary Feature Detection

As the demand of wireless communication increases exponentially, with the same ratio scarcity of spectrum also originates. To overcome this spectrum scarcity a novel approach, Cognitive Radio (CR) shows development of an opportunistic and promising technology. This paper explores implementation and analysis of the CR spectrum sensing techniques such as Matched filtering, Energy detection and Cyclostationary feature detection on MATLAB platform by simulation. We analyze performance of these techniques over, Nakagami-m fading channel with AWGN channel for both the BPSK and QPSK modulation.

scholarly journals Spectrum Sensing using AMC and TFT

2019 ◽  
Vol 9 (2) ◽  
pp. 898-902
Keyword(s):  
Decision Making ◽  
Spectrum Sensing ◽  
Fading Channel ◽  
Matched Filter ◽  
Energy Detection ◽  
Primary User ◽  
Modulation Classification ◽  
Time Frequency ◽  
Automatic Modulation Classification ◽  
Channel Conditions

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.

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