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

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

3. 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.

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

5. 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

6. 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

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

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

9. TRANSMITTER DESIGN

10. ENERGY DETECTION BASED

11. SIMULATED RESULT USING BPSK MODULATOR

12. SIMULATED RESULT USING QPSK MODULATOR

13. MATCHED FILTER BASED

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

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

16. CYCLOSTATIONARY BASED

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

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

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

20. 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
sec
sec
sec 2
QPSK
sec
sec
sec

21. 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

22. 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

23. 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.

24. 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.

25. Reference
A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications 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 , Nov
S. Haykin, Cognitive radio: brain-empowered wireless communications, IEEE Journal on Selected Areas in Communications 23 (2) (2005) 201–220

26. THANK yOU