Design and analysis of spectrum sensing in cognitive radio based on energy detection

Cognitive Radio is an intelligent radio designed to automatically detect the best wireless channel in wireless spectrum that is unused at a specific time. It is software defined radio in which the operating parameters such as modulation type, power or spectrum band change by software. Spectrum sensing is one of the most important functions of cognitive radio. It is used to detect the activity of the licensed users in a selected band. This paper used the Energy detection method for spectrum sensing to sense the presence of primary users. The energy detection algorithm is coded in MATLAB to determine the availability of used spectrum of licensed band. Probability of detection is calculated as the ratio between the number of times the signal passes the threshold value and the total number of trails using Monte-Carlo method. Three different modulation techniques BPSK, QPSK and QAM under Additive White Gaussian Noise (AWGN) are specified for increasing signal quality and detection. In this work PD of primary user at lower signal to noise ratio up to −10dB with fixed Probability of false alarm at 0.00001, 0.0001, 0.001 and 0.01 is analyzed. After analysis it is observed that PD increases with increase in PFA. It is also observed that among these modulation techniques, QAM is the best method for the spectrum sensing.

[1]  Sudhir G. Akojwar,et al.  Implementation of Energy Detection Method for Spectrum Sensing in Cognitive Radio Based Embedded Wireless Sensor Network Node , 2014, 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies.

[2]  Nima Reisi,et al.  Performance Analysis of Energy Detection-Based Spectrum Sensing over Fading Channels , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[3]  Chen Li,et al.  Distributed Compressive Wideband Spectrum Sensing in Cooperative Multi-Hop Cognitive Networks , 2010, 2010 IEEE International Conference on Communications.

[4]  Jianhua Shao,et al.  Designment and implementation of cognitive radio model based on FPGA , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[5]  B. Sindhuja,et al.  Implementation of an adaptive spectrum sensing technique in cognitive radio networks , 2015, 2015 International Conference on Computing and Communications Technologies (ICCCT).

[6]  Ranjitha Kumar,et al.  Spectrum Sensing In Cognitive Radio Using Matlab , 2013 .

[7]  Hossam M. Farag,et al.  An efficient dynamic thresholds energy detection technique for Cognitive Radio spectrum sensing , 2014, 2014 10th International Computer Engineering Conference (ICENCO).

[8]  Sherali Zeadally,et al.  Spectrum Assignment in Cognitive Radio Networks: A Comprehensive Survey , 2013, IEEE Communications Surveys & Tutorials.

[9]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[10]  Harpreet Kaur,et al.  VHDL Implementation of FFT/IFFT Blocks for OFDM , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[11]  Ian F. Akyildiz,et al.  A survey on spectrum management in cognitive radio networks , 2008, IEEE Communications Magazine.

[12]  Sampath Rangarajan,et al.  Energy Detection Based Spectrum Sensing for Cognitive Radio: An Experimental Study , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[13]  Aslam Durvesh,et al.  Energy Detection Based Spectrum Sensing for Cognitive Radio Network , 2015, 2015 Fifth International Conference on Communication Systems and Network Technologies.