1. Adaptive Double-Threshold Based Energy and Matched Filter Detector in Cognitive Radio Networks
Ashish Rauniyar, Soo Young Shin
IT Convergence Engineering
Kumoh National Institute of Technology
South-Korea
2014 International Workshop on Wireless, Mobile and Sensor Networks
(IWMSN 2014)

2. Content Introduction System Description Simulation Result Conclusion
Contribution
System Description
Energy Detector
Matched Filter Detector
Related Works
Proposed Model
Algorithm
Simulation Result
Conclusion

3. Introduction Cognitive Radio (CR)
Provides effective solution for opportunistic access to unused licenced spectrum bands.
CR allows secondary users (SU) to utilize the free portions of licensed spectrum while ensuring no interference to primary users (PU) transmissions.
Contributions
Proposed Adaptive Double-Threshold Based Energy and Matched Filter Detector for Cognitive Radio Networks.
Employing energy efficient Energy Detector and reliable Matched Filter Detector.

4. Introduction Cognitive Radio Network
CR provide high level of protection to the primary users (PU) from secondary users (SU).
In CSS, the fusion centre collects the local decisions of the SUs and makes the final decision to ascertain whether the PU is present or not.
Cooperative Spectrum Sensing
The pictures are taken from Innovation Lab website, Stevens Institute of Technology, USA

5. Energy Detector (ED)
ED is a non-coherent detection device with low implementation complexity and is more power efficient.
ED performance is highly degraded under noise uncertainty condition.
The expression for pfa and pd for ED can be given as
where,
Fx is cumulative distribution function of standard chi-square random variable.
Q is generalized Marcum-Q function.
is threshold for the ED is the variance.

6. Matched Filter (MF) where
MF is a reliable detector but consumes high amount of power.
MF works using receivers bank of L matched filters, which runs together to correlate the incoming signals.
The expression for pfa and pd for MF can be given as
where
Fx is cumulative distribution function of standard chi-square random variable.
Q is generalized Marcum-Q function.
is threshold for the MF.

7. Energy Distribution of Primary User’s Signal and Noise

8. Related Works (1/2)
A censoring method using double threshold based on ED was proposed in [9].
If the detected observational energy values (Oi) by the SU lies in the confused region, they will not report to the fusion center.
This method can reduce the sensing time but causes sensing failure problem.
[9] Chunhua Sun, Wei Zhang, and Khaled Letaief. Cooperative spectrum sensing for cognitive radios under bandwidth constraints. In Wireless Communications and Networking Conference, WCNC IEEE, pages 1–5. IEEE, 2007.

9. Related Works (2/2)
[10] also proposed a method using double threshold based on ED to increase the detection performance as compared to the conventional ED.
If the Oi lies in the confused region then the SU will forward it to the fusion center (FC).
The FC will make overall decision by considering the local decision of SU of clear region and comparing the Oi of confused region with another threshold value of ED.
[10] Jiang Zhu, Zhengguang Xu, Furong Wang, Benxiong Huang, and Bo Zhang. Double threshold energy detection of cooperative spectrum sensing in cognitive radio. In Cognitive Radio Oriented Wireless Networks and Communications, CrownCom rd International Conference on, pages 1–5, May 2008.

10. Proposed Method Using Energy and Matched Filter Detector
The main idea of our proposed scheme is that we take the advantage of energy efficient ED to make decision in the clear region and reliable MF to make decision in the confused region.

11. Working Model of proposed Adaptive Double-Threshold Based on Energy and Matched Filter Detector

12. Algorithm- Adaptive Double-Threshold Based Energy and Matched Filter Detector Method

13. Proposed Algorithm Explanation
The observational value Oi of SU is checked with the threshold values and of ED and the decision will be taken accordingly.
If Oi satisfies then no decision will be taken and SU will forward its Oi to the fusion center.

14. Proposed Algorithm Explanation
We assume that fusion center FC receives K local decisions out of N SUs.
The fusion center will now apply more reliable MF on those N-K observational values of the signal for the decision process.
The decision D at the FC using the MF detector on N-K Oi is

15. Proposed Algorithm Explanation
The FC has the local decision Li of K SUs using ED and decision D of N-K SUs using MF.
Let us denote the total decision at FC by Z.
Then
The final mathematical expression of hypothesis at the FC can be written as

16. Cooperative Probability of Detection and Miss-Detection
The cooperative probability of detection Qd of the FC using OR rule is given as
where
u is the time bandwidth product.
is probability of No Decision under Hypothesis H1.
The cooperative probability of miss-detection Qm of the FC is given by

17. Cooperative Probability of False Alarm
The cooperative probability of False Alarm Qf of the FC using OR rule is given as
where,
u is the time bandwidth product.
is probability of No Decision under Hypothesis H0.

18. Simulation Parameters
AWGN is imposed on the original signal xi either for H0 or H1 condition.
SNR = 10dB
Number of cooperative SUs=10
Time bandwidth product u =5

19. Simulation Results (1/3)

20. Simulation Results (2/3)

21. Simulation Results (3/3)

22. Conclusion
This paper investigated the Energy Detector (ED) and Matched Filter (MF) Detector.
A new adaptive double-threshold based on ED and MF for Cognitive Radio Networks was proposed.
The proposed scheme takes the advantage of energy efficient energy detector to take decision in the clear region and reliable matched filter detector to take decision in the confused region.
Simulation results showed that the proposed method gives significantly better detection performance compared to other methods.

23. Thank You Q&A Ashish Rauniyar (ashish.rauniyar@kumoh.ac.kr)