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Harish presentation

  1. 1. Performance Evaluation of Local and Cooperative Spectrum Sensing in Cognitive Radio by Harish Barvekar Roll No-11EC64R17 (M.Tech TSE) Under the supervision of Dr. Saswat Chakrabarti Department of Electronics & Electrical Comm. Engg. Indian Institute of Technology, Kharagpur, India
  2. 2. Contents  Introduction  Problem Definition  Implementation of Energy detection technique  Implementation of One-Order Cyclostationary Feature Detector  Implementation of Two-Order Cyclostationary Feature Detector  Implementation of Cooperative spectrum sensing using energy detector  Implementation of spectrum management and sharing concept  Conclusion  Future Work  References 2
  3. 3. Background  most of the radio frequency spectrum inefficiently utilized  spectrum utilization depends strongly on time and place  fixed spectrum allocation wastes resources  improved efficiency by allowing unlicensed users to exploit spectrum whenever it would not cause interference to licensed users 3
  4. 4. White Spaces McHenry. M. A., “NSF Spectrum occupancy measurements project summary,” Shared Spectrum Company, Tech. Rep., Aug. 2005. 4
  5. 5. Functions of Cognitive Radio  Spectrum Sensing  Spectrum Management  Spectrum Sharing  Spectrum Mobility 5
  6. 6. Types of spectrum sensing6
  7. 7. Problem Definition  Implementation and performance evaluation of local spectrum sensing techniques like Energy detector, One-order Cyclostationary feature detection and Two-order Cyclostationary feature detection  Implementation of Cooperative spectrum sensing through energy detector  Implementation of spectrum management and spectrum sharing characteristics with the help of posix Pthread program in Linux platform 7
  8. 8.  Signal Model8
  9. 9. Received Signal Strength [6] 9
  10. 10.  10
  11. 11. Implementation of Energy detection technique Block diagram of an energy detector Following the work of Urkowitz [13], Y may be shown to have the following distribution 11
  12. 12.  12
  13. 13. 1. Initialize number of samples N, prob. of false alarm and avg. S/N 2. Initialize counter to zero 7. Counter = counter + 1 3. Generate an input signal and add additive white Gaussian noise to it 4. Calculate the threshold value using known set of probability of false alarm 6.Is Energy of s/g > threshold 8.Calculate the probability of detecting the signal by dividing the final value counter with the number of Monte –Carlo iterations 9.Calculate the value probability of miss detection by subtracting the values of probability of detection from the integer 1 10.Plot the graph between probability of false alarm and probabilityof miss detection 5. Calculate the energy of the noisy input signal Repeat Steps 3-6 for Monte-Carlo times Flowchart of algorithm 13
  14. 14. Simulation Result Complementary Receiver Operating Characteristic (CROC) curve of Energy detection technique over AWGN channel (a) theoretical (b) as obtained through simulation 14
  15. 15. Implementation of One-Order Cyclostationary Feature Detector  One-order Cyclostationary feature detection 15
  16. 16.  16
  17. 17. Simulation Result Complementary Receiver Operating Characteristic (CROC) curve of one- order Cyclostationary detection technique over AWGN channel (a) theoretical (b) as obtained through simulation 17
  18. 18. Implementation of Two-Order Cyclostationary Feature Detector Two-order Cyclostationary detection technique 18
  19. 19.  19
  20. 20. Simulation Result Complementary Receiver Operating Characteristic (CROC) curve of two-order Cyclostationary detection technique over AWGN channel (a) theoretical (b) as obtained through simulation 20
  21. 21. Implementation of Cooperative spectrum sensing using energy detector  Every CR performs its own local spectrum sensing measurements independently and then makes decision on whether the PU is present or not  All of the CRs forward their decisions to a common receiver  The common receiver fuses the CR decisions and makes a final decision to infer the absence or presence of the PU 21
  22. 22.  22
  23. 23. Simulation Result Receiver Operating Characteristics (ROC) curve of Cooperative spectrum sensing with OR rule under AWGN channel 5 CRs are used for local sensing23
  24. 24. Receiver Operating Characteristics (ROC) curve of Cooperative spectrum sensing with OR rule under AWGN Channel with CR=1, 2, 3,4 and 5 24
  25. 25. Implementation of spectrum management and sharing concept Challenges  after detection of spectrum holes, how to allocate channels to the secondary users  how to handle the situation, when primary user comes back for transmission Selection criteria  User is selected by their priority  if priority equals, then user is selected by their burst time 25
  26. 26. Flowchart of Spectrum sharing program 26
  27. 27. S1 B3 S3 B6 S2 B5 S1 B4 Communication channel Priority queue Primary user Secondary user
  28. 28. S3 B6 S2 B5 S4 B9 S1 B3 Communication channel Priority queue Primary user Secondary user
  29. 29. S3 B6 S2 B5 S4 B9 S1 B3 S5 B7 Communication channel Priority queue Primary user Secondary user
  30. 30. S3 B6 S2 B5 S4 B9 S1 B3 S5 B7 Communication channel Priority queue Primary user Secondary user
  31. 31. S3 B6 S2 B5 S4 B9 S5 B7 S1 B2 Communication channel Priority queue Primary user Secondary user
  32. 32. S3 B6 S2 B5 S4 B9 S5 B7 S1 B2 Communication channel Priority queue Primary user Secondary user
  33. 33. S3 B6 S2 B5 S4 B9 S5 B7 S1 B2 P1 B6 Communication channel Priority queue Primary user Secondary user
  34. 34. S3 B6 S2 B5 S4 B9 S5 B7 S1 B2 P1 B6 Communication channel Priority queue Primary user Secondary user
  35. 35. S3 B6 S2 B5 S4 B9 S5 B7 S1 B2 P1 B5 P1 B6 Communication channel Priority queue Primary user Secondary user
  36. 36. S3 B6 S2 B5 S4 B9 S5 B7 S1 B2 P1 B5 Communication channel Priority queue Primary user Secondary user
  37. 37. Simulation Result
  38. 38. Conclusion  The complementary receiver operating characteristic curve for the spectrum sensing techniques are studied  The receiver operating characteristic curve for cooperative spectrum sensing using energy detector is also studied  The concept of spectrum management and sharing is studied by a software Pthread program 40
  39. 39. Future Work  The work can be extended to a scenario where multiple primary users are involved  Security can be enhanced by detecting the malicious user in the Cognitive radio network and remove their decision  Concept of primary user emulation attack can also be introduced in cooperative spectrum sensing 41
  40. 40. References [1] J. Mitola and G. Q. Maguire, “Cognitive radio: Making Software Radios More Personal,” IEEE Pers, Commun., vol. 6, pp. 13–18, Aug. 1999. [2] McHenry. M. A., “NSF Spectrum occupancy measurements project summary,” Shared Spectrum Company, Tech. Rep., Aug. 2005. [3] I. F. Akyildiz, Y. Altunbasak, F. Fekri, and R. Sivakumar, “AdaptNet: an adaptive protocol suite for the next- generation wireless internet,” IEEE Commun. Mag., pp. 128–136, Mar. 2004. [4] Federal Communications Commission, “Notice of Proposed Rule Making and Order,” Rep. ET Docket no.03-322, Dec. 2003. [5] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201 - 220, Feb. 2005 [6] M. Ghozzi, M. Dohler, F. Marx, and J. Palicot, “Cognitive radio: methods for the detection of free band,” Comptes Rendus Physique, Elsevier, vol. 7, pp. 794–804, Sep. 2006. [7] Fadel F. Digham, Mohamed-Slim Alouini, and Marvin K. Simon,” On the Energy Detection of Unknown Signals over Fading Channels” IEEE Transactions On Wireless Communications, vol. 7, no. 12, pp. 3575 – 3579, Dec. 2008 [8] EkramHossain, DusitNiyato and Zhu Han, “Dynamic Spectrum Access and Management in Cognitive Radio Networks”, Cambridge university Press, 2009 [9] Y. Wen-jing, Z. Bao-yu, M. Qing-min,” Cyclostationary property based Spectrum sensing algorithms for primary detection in Cognitive Radio systems,” Institute of Signal Processing and Transmission, Nanjing 2100003, China, 2008 42
  41. 41. [10] C. Sun, W. Zhang, and K. B. Letaief, “Cluster-Based Cooperative Spectrum Sensing in Cognitive Radio Systems,” Hong Kong University of Science and Technology, 2007 [11] Lei Zhang and Zhijun Xiao, “Performance Analysis of Cooperative Spectrum Sensing Algorithm for Cognitive Radio Networks,” International Conference on Computer Design and Applications (ICCDA 2010), vol.4, pp.V4-557- V4-560, 25-27, June 2010 [12] Xuping. Zhai, Jiango. Pan, “Energy-Detection Based Spectrum Sensing for Cognitive Radio, Wireless, Mobile and Sensor Networks,” IET Conference, 2007 [13] H. Urkowitz, “Energy Detection of Unknown Deterministic Signals,” IEEE, vol.55, pp.1606, April 2002 [14] Chao Chen, Hongbing Cheng and Yu-Dong Yao, “Cooperative Spectrum Sensing in Cognitive Radio Networks in the Presence of the Primary User Emulation Attack,” IEEE Transactions on Wireless Communications, vol.10, Issue 7, pp.2135 – 2141,July 2011 [15] F. Visser, G. Janssen, P. Pawelczak, “Multinode Spectrum Sensing Based on Energy Detection for dynamic Spectrum Access,” IEEE, pp. 1394-1398, 2008 [16] D. Duan, L. Yang and J. C. Principe, “Cooperative Diversity of Spectrum Sensing for Cognitive Radio Systems,” IEEE transactions on signal processing, vol. 58, no. 6, June 2010 [17] Y. Gao and Y. Jiang, “Performance Analysis of A Cognitive Radio Network With Imperfect Spectrum Sensing”, IEEE Infocom 2010, pp.1-6, Issue date: 15-19, March 2010 [18] Paisana F., Prasad N., Rodrigues A., Prasad R., “An alternative implementation of a cyclostationary detector,” Wireless Personal Multimedia Communications (WPMC), pp. 45-49, 2012 43

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