近年來因為通訊科技的快速發展，使得有限的頻譜資源越顯不足，因此認知型無線電(Cognitive Radio)以其可重複利用頻譜的特點，受到越來越高的重視。認知型使用者為了重複使用頻譜空檔(spectrum hole)，必須先做頻譜偵測的動作以建立自己的傳輸連線，而一個使用者的偵測結果會因為隱性端點問題(hidden terminal problem)而變得不夠牢靠，於是合作式頻譜偵測被提出以減緩偵測之不盡可靠性，但此偵測方式會使得閒置中之認知型使用者(idle user)的能量消耗增加，因為他們即使在本身不進行資料傳輸時也必須幫助他人偵測頻譜；為此，應用於認知型無線電中之頻譜偵測處理器應具有節能的特點，使得閒置使用者不會將其大部份能量都消耗在合作式偵測之上。我們的論文提出了應用於認知型無線電中合作式頻譜偵測節能離散小波包裹轉換處理器，我們所提出的處理器採用了雙重臨界(double-threshold)的方式增加了偵測的可靠性，根據模擬結果，我們的設計比起傳統單一臨界方法，可省下至少20%的所需乘法數；另外，當頻譜上存在一定程度的相關性時，我們可使用「偵測結果AND規則預測法」(AND-rule Detection Result Prediction, AND-rule DRP)使得偵測結果更加可靠，使用者可依據環境不同選擇是否使用預測法；IEEE 802.22 WRAN是為關於認知型無線電之通訊規格，其中有此規格對於錯誤率之最低要求，我們的模擬結果顯示，不管有沒有使用偵測結果AND規則預測法，我們所提出的頻譜偵測處理器都可以滿足此規格之要求。

1 Introduction
1.1 Cognitive Radio
1.2 Research Motivation
1.3 Organization of This Thesis

2 System Model
2.1 Channel Model
2.1.1 Rayleigh Fading Channel
2.1.2 Doppler Effect
2.1.3 Multipath Effect
2.2 Primary User Signal Pattern
2.3 Detection Error Probability and False Alarm Rate
2.4 Energy Detector
2.5 Decision Threshold
2.6 Cooperative Spectrum Sensing

3 Partial Cooperative Spectrum Sensing
3.1 Discrete Wavelet Packet Transform
3.2 Full Spectrum Sensing
3.3 Double-Threshold Partial Spectrum Sensing
3.4 Partial DWPT-based Energy Detector
3.5 Computational Complexity Comparison
3.6 DRP and DRM

4 Simulation Results
4.1 Modified Threshold Fitting
4.2 Double Threshold Partial Spectrum Sensing
4.2.1 DFS Timing Requirement
4.2.2 Detection Threshold
4.2.3 Multipath Effect
4.2.4 Required Multiplications
4.3 Cooperative Spectrum Sensing
4.3.1 DRP and DRM
4.3.2 Different Modulation
4.3.3 Multipath Profile
4.4 Different Signal Patterns

5 Partial DWPT Processor Implementation
5.1 Introduction
5.1.1 Lifting Scheme
5.1.2 Poly-phase IIR Structure
5.1.3 DWPT Processor Architecture
5.2 Partial DWPT-based Spectrum Sensing
5.2.1 Partial DWPT Processor
5.2.2 Preprocessing (DRP)
5.2.3 Fusion Center
5.3 Timing Schedule
5.4 Fixed-point Simulation
5.5 RTL Verification

6 Conclusion

[1] FCC, “Spectrum policy task force,” Tech. Rep. ET Docket no. 02-135, Nov 2002.
[2] R. Chiang, G. Rowe, and K. Sowerby, “A quantitative analysis of spectral occupancy measurements for cognitive radio,” Vehicular Technology Conference, 2007.
VTC2007-Spring. IEEE 65th, pp. 3016–3020, April 2007.
[3] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” Selected Areas in Communications, IEEE Journal on, vol. 23, no. 2, pp. 201–220, Feb. 2005.
[4] Z. Quan, S. Cui, H. Poor, and A. Sayed, “Collaborative wideband sensing for cognitive radios,” Signal Processing Magazine, IEEE, vol. 25, no. 6, pp. 60 –73,
2008.
[5] K. Ben Letaief and W. Zhang, “Cooperative communications for cognitive radio networks,” Proceedings of the IEEE, vol. 97, no. 5, pp. 878 –893, May 2009.
[6] E. Peh and Y.-C. Liang, “Optimization for cooperative sensing in cognitive radio networks,” in Wireless Communications and Networking Conference, 2007.WCNC
2007. IEEE, 2007, pp. 27 –32.
[7] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” Communications Surveys Tutorials, IEEE, vol. 11, no. 1, pp.
116 –130, 2009.
[8] B. Wild and K. Ramchandran, “Detecting primary receivers for cognitive radio applications,” in New Frontiers in Dynamic Spectrum Access Networks, 2005. DyS-
PAN 2005. 2005 First IEEE International Symposium on, 2005, pp. 124 –130.
[9] D. Cabric, S. Mishra, and R. Brodersen, “Implementation issues in spectrum sensing for cognitive radios,” in Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on, vol. 1, 2004, pp. 772 – 776 Vol.1.
[10] H. Arslan, Ed., Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems. Springer, 2007.
[11] G. Atia, E. Ermis, and V. Saligrama, “Robust energy efficient cooperative spectrum sensing in cognitive radios,” Statistical Signal Processing, 2007. SSP ’07. IEEE/SP 14th Workshop on, pp. 502–506, Aug. 2007.
[12] E. Visotsky, S. Kuffner, and R. Peterson, “On collaborative detection of tv transmissions in support of dynamic spectrum sharing,” in New Frontiers in Dynamic
Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on, 2005, pp. 338 –345.
[13] C. Sun, W. Zhang, and K. Letaief, “Cooperative spectrum sensing for cognitive radios under bandwidth constraints,” in Wireless Communications and Networking
Conference, 2007.WCNC 2007. IEEE, 2007, pp. 1 –5.
[14] E. Visotsky, S. Kuffner, and R. Peterson, “Guess: gossiping updates for efficient spectrum sensing,” in International workshop on Decentralized resource sharing in
mobile computing and networking, 2006.
[15] N. Neihart, S. Roy, and D. Allstot, “A parallel, multi-resolution sensing technique for multiple antenna cognitive radios,” Circuits and Systems, 2007. ISCAS 2007.
IEEE International Symposium on, pp. 2530–2533, May 2007.
[16] W.-B. Chien, C.-K. Yang, and Y.-H. Huang, “Energy-saving cooperative spectrum sensing processor for cognitive radio system,” Circuits and Systems I: Regular Papers, IEEE Transactions on, 2010.
[17] Y. Youn, H. Jeon, H. Jung, and H. Lee, “Discrete wavelet packet transform based energy detector for cognitive radios,” in Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th, 2007, pp. 2641 –2645.
[18] C. Wang and W. S. Gan, “Efficient vlsi architecture for lifting-based discrete wavelet packet transform,” Circuits and Systems II: Express Briefs, IEEE Transactions on, vol. 54, no. 5, pp. 422 –426, May 2007.
[19] J. G. Proakis, Digital communications, 4th ed. McGraw-Hill, 2001.
[20] C. Xiao, Y. Zheng, and N. Beaulieu, “Second-order statistical properties of the wss jakes’ fading channel simulator,” Communications, IEEE Transactions on, vol. 50,
no. 6, pp. 888 –891, Jun. 2002.
[21] S. H. Hwang, J. S. Um, M. S. Song, C. J. Kim, H. R. Park, and Y. H. Kim, “Design and verification of ieee 802.22 wran physical layer,” in Cognitive Radio Oriented
Wireless Networks and Communications, 2008. CrownCom 2008. 3rd International Conference on, May 2008, pp. 1 –6.
[22] C. R. Stevenson, C. C. Eli Sofer, and G. Chouinard, Functional Requirement for the 802.22 WRAN Standard, Std. IEEE 802.22-05-0071r47, Jan 2006.
[23] UNITED STATES FREQUENCY ALLOCATIONS, National Telecommunications and Information Administration, U.S. DEPARTMENT OF COMMERCE Std., Oct 2003. [Online]. Available: http://www.ntia.doc.gov/osmhome/allochrt.pdf
[24] FCC ONLINE TABLE OF FREQUENCY ALLOCATIONS, FEDERAL COMMUNICATIONS COMMISSION Std., Jan.
[25] Digital Video Broadcasting (DVB); Framing structure, channel coding and modulation for digital terrestrial television, ETSI Std. EN 300 744 V1.6.1, Jan. 2009.
[26] H. Tang, “Some physical layer issues of wide-band cognitive radio systems,” New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First
IEEE International Symposium on, pp. 151–159, Nov. 2005.
[27] D. Kadhim, S. Gong, W. Liu, and W. Cheng, “Optimization of cooperation sensing spectrum performance,” in Communications and Mobile Computing, 2009. CMC’09. WRI International Conference on, vol. 1, 2009, pp. 78 –82.
[28] J. Hammersley and D. Handscomb, Monte Carlo Methods. METHUEN, 1975.
[29] G. Strang and T. Nguyen, Wavelets and Filter Banks, 1996.
[30] A. V. Oppenheim, R. W. Schafer, and J. R. Buck, Discrete-time Signal Processing, 2nd ed. Prentice Hall, 1999.
[31] A. N. Akansu, Ed., Subband and Wavelet Transforms: Design and Application. Kluwer Academic Publishers, 1996.
[32] I. Daubechies, Ten Lectures on Wavelets. The Society for Industrial and Applied Mathematics, 1992.
[33] C. Wang and W. S. Gan, “Efficient vlsi architecture for lifting-based discrete wavelet packet transform,” Circuits and Systems II: Express Briefs, IEEE Transactions on, vol. 54, no. 5, pp. 422 –426, May 2007.
[34] I. Daubechies and Sweldens, Factoring wavelet transforms into lifting steps, vol. 4, pp. 247 –269, 1998.
[35] W.-B. Chien, “Design and implementation of an energy-saving cooperative spectrum sensing processor for cognitive radio,” Master’s thesis, National Tsing Hua
University, Hsinchu, Taiwan, 2010.