PERFORMANCE ANALYSIS OF SPECTRUM SENSING USING COGNITIVE RADIO PROJECT GUIDE: PROJECT MEMBERS: Mr.C.S.KARTHIKEYAN S.HARIHARAPANDIAN(91708106036) Asst.prof/ ECE J.NELSONSUNDARAJ(91708106058) PS.SUNDARRAJAPANDIAN(91708106308)

OBJECTIVE To implement spectrum sensing by Transmitter Detection mechanism using Energy based detection, Matched filter detection, Cyclostationary detection methods and to compare their performance.

ABSTRACT Our project is based on recent research which shows that, more than 70% of the available spectrum is not utilized efficiently. To overcome this we are going to the concept of cognitive radio. It offers a solution by utilizing the spectrum holes that represent the potential opportunities for non- interfering use of spectrum.

BASE PAPER A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications- 2009 by Tevfik Y¨ucek and H¨useyin Arslan

Algorithm Transmitter of primary user is designed Implementation of detection technique for spectrum sensing. Energy detection Matched Filter detection Cyclostionary Feature detection Comparison & Analysis of the implemented techniques

COGNITIVE RADIO Cognitive Radio is a system for wireless communication, It is built on software defined radio which is an emerging technology providing a platform for flexible radio systems It uses the methodology of sensing and learning from the environment and adapting to statistical variations in real time The network changes its transmission /reception parameters to communicate efficiently anywhere and anytime avoiding interference with licensed or unlicensed users for efficient utilization of the radio spectrum

COGNITIVE CYCLE A basic cognitive cycle comprises of following three basic tasks: 1.Spectrum Sensing 2.Spectrum Analysis 3.Spectrum Decision Making

Transmitter Detection Receiver Detection Spectrum Sensing Transmitter Detection Receiver Detection Interference Temperature Management Energy Detection Matched Filter Detection Cyclostationary Detection

TRANSMITTER DESIGN

ENERGY DETECTION BASED

SIMULATED RESULT USING BPSK MODULATOR

SIMULATED RESULT USING QPSK MODULATOR

MATCHED FILTER BASED

SIMULATED RESULT USING BPSK MODULATOR SNR Vs Correlated value of Rx signal and carrier signal

SIMULATED RESULT USING QPSK MODULATOR SNR Vs Correlated value of Rx signal and carrier signal

CYCLOSTATIONARY BASED

SIMULATED RESULT USING BPSK MODULATOR Frequency Vs correlated values of received signal

SIMULATED RESULT USING QPSK MODULATOR Frequency Vs correlated values of received signal

Comparison of Transmitter Detection Techniques Metrics for Comparison Sensing Time Ease for Implementation

Sensing Time 1 BPSK 7.756002 sec 5.041366 sec 11.884458 sec 2 QPSK No. Type of Primary Signal Energy Detection Matched Filter Cyclostationary 1 BPSK 7.756002 sec 5.041366 sec 11.884458 sec 2 QPSK 5.200327 sec 4.173570 sec 8.089197 sec

EASE OF IMPLEMENTATION S.NO TYPE ENERGY DETECTION MATCHED FILTER CYCLOSTATI-ONARY 1) SENSING TIME MORE LESS MOST 2) SIMPLE TO IMPLEMENT YES NO 3) PERFORMA-NCE UNDER NOISE POOR BAD GOOD 4) PRIOR KNOWLEDGE REQUIRED

Comparison of spectrum sensing methods Advantages Disadvantages Matched filter- Probability of false alarm or missing detection is low Require perfect knowledge of PU,Large power consumption. Energy detector-based sensing Low computational and implementation complexities Inability to differentiate interference from primary users and noise. Poor performance under low SNR Cyclostationarity-Based Sensing Differentiate noise from PU. Bandwidth efficiency High computational complexity

CONCLUSION Dynamic spectrum access is a solution for spectrum shortage by allowing unlicensed users to use spectrum holes across the licensed spectrum for that the detection and classification of primary user’s waveform in cognitive radio networks was done using three spectrum sensing methods. By comparing these methods it is concluded that Cyclostationary feature detection gives best results but take long computation time compared to other techniques.

FUTURE WORK A fuzzy logic based algorithm minimizing sensing time and improving reliability can be implemented which gives very good results at high SNR values. It will take long computation time but it will give reliable results. However, accurate detection is to be predicted, the computational time can be sacrificed for accuracy of detection. Moreover for actual implementation, the technique can be implemented on real time processing hardware.

Reference A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications- 2009 by Tevfik Y¨ucek and H¨useyin Arslan Simulation and software radio for mobile communication ,Hiroshi Harada and Ramjee Prasad. H. Tang,”Some Physical Layer Issues of Wideband Cognitive Radio System”,in Proc. IEEE DySPAN, pp. 151159, Nov. 2005. S. Haykin, Cognitive radio: brain-empowered wireless communications, IEEE Journal on Selected Areas in Communications 23 (2) (2005) 201–220

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