感知無線電(cognitive radio)為一個有效提升無線通訊頻譜使用效率的技術。在感知無線電系統中，次要使用者(secondary user)可藉由頻譜偵測(spectrum sensing)技術來有效利用主要使用者(primary user)的頻帶。然而無線通道的遮蔽(shadowing)與衰減(fading)的效應，造成單一次要使用者無法提供可靠的偵測效能；為了解決此問題，合作式頻譜偵測(cooperative spectrum sensing)的方法已被提出。傳統合作式頻譜偵測方法中，當次要使用者數目較多時，會造成回傳偵測結果所需的頻寬負載增加。另一方面，傳統合作式方法中，對所有次要使用者所採用的誤警機率(probability of false alarm)都相同，亦即即使有部分次要使用者處在較差的衰落通道中，被分配的到偵測可信度依然和處在較佳的通道的次要使用者相同，此狀況會明顯影響到偵測上的效能。
在此篇論文中，我們將探討如何有效降低回傳所需的偵測位元數且增加偵測上的效能，兩階段之合作式頻譜偵測的方法首先被提出來改進回傳的頻寬效率，此方法基本的概念為假使有一個或多個可靠的次要使用者於第一階段被系統所設定的嚴格的偵測門檻值給篩選出來，系統會立即判定主要使用者為存在，否則偵測程序將進入第二階段，此時滿足第二階段所需條件的次要使用者會回傳軟性資訊至基地台來進行偵測，此方法可避免不必要的偵測位元數的發生。
接著為了改進偵測上的效能，我們提出信任度之合作式頻譜偵測的方法，此方法會根據先前的偵測結果來估算出每個次要使用者的偵測可靠度，並用來調整誤警機率，藉由分配較高的誤警機率於較可靠的次要使用者身上，除了達到較佳的合作式頻譜偵測的效能，還可易於系統上的分析。最後，結合信任度和兩階段偵測方法，可同時改進感知無線電系統的頻譜利用率和頻寬效率。
為了有效評估此篇論文所提出方法的偵測效能，我們提供在雷利衰減通道(Rayleigh fading channel)下之偵測機率與平均偵測位元數的理論分析，且模擬結果接近於理論分析。經由模擬結果我們可以看出，比較雙門檻值偵測方法，所提出的方法可達到較佳的偵測效能與較少的平均偵測位元數。

Cognitive radio (CR) is an emerging technology for enhancing the efficiency of wireless spectrum utilization. In cognitive radio systems, secondary users (SUs) are allowed to access the frequency bands of a primary user (PU) by spectrum sensing. However, a single SU usually cannot provide robust sensing performance due to the effects of hidden nodes, shadowing, and fading channels. In order to overcome this problem, cooperative spectrum sensing techniques have been proposed in the literature. In conventional cooperative spectrum sensing schemes, the required bandwidth of feedback channels become larger with the SUs increased. Moreover, all the SUs are required to have an identical probability of false alarm, that is, the CR system supposes that all the sensing nodes have the same sensing reliability even though some of them are suffering from a deep fade. In this case, the detection performance will be degraded.
This thesis investigates how to reduce the average number of sensing bits as well as to improve the detection performance. A two-step detection method for cooperative spectrum sensing is first proposed for improving the bandwidth efficiency over feedback channels. The basic idea is that if one or more reliable SUs have been sifted out during the first step by a strict decision threshold, then the CR system declares that the PU is active at once and no second-step operations are required; otherwise, the detection process will get into the second step, where the SUs transmit their soft information to the cognitive radio base station for further detection. In this way, unnecessary feedback bits transmitted from those unreliable SUs can be avoided.
In order to improve the detection performance, a credibility method for cooperative spectrum sensing is also proposed. This method evaluates the sensing reliability of SUs based on previous sensing results, and then adjusts the probability of false alarm of each SU. Assigning a higher probability of false alarm for a more reliable SU, we can have better detection performance for cooperative spectrum sensing. With the credibility method and the two-step detection method, we can improve the spectrum utilization and the bandwidth efficiency of CR systems simultaneously.
To verify the effectiveness of the proposed cooperative spectrum sensing scheme, the probability of false alarm, the probability of detection, and the average number of sensing bits are analyzed under Rayleigh fading channels, where the results are close to those obtained from simulations. All these support that the proposed approach outperforms the double threshold detection method in terms of the detection performance and the average number of sensing bits.

Abstract i
Contents iii
List of Figures v

Chapter 1 Introduction 1
1.1 Background 1
1.2 Spectrum Hole 2
1.3 Cognitive Radio 2

Chapter 2 Spectrum Sensing Techniques 6
2.1 Problem Formulation 6
2.2 Local Spectrum Sensing 7
2.2.1 Matched Filter Detector 8
2.2.2 Cyclostationary Feature Detector 8
2.2.3 Energy Detector 10
2.3 Cooperative Spectrum Sensing 12
2.3.1 Cooperative Spectrum Sensing with Soft Combining Method 13
2.3.2 Cooperative Spectrum Sensing with Hard Combining Method 17
2.3.3 Cooperative Spectrum Sensing with Soft-Hard Combining Method 18
2.4 Motivation 21

Chapter 3 The Proposed Schemes for Cooperative Spectrum Sensing 24
3.1 System Model 25
3.2 The Proposed Schemes 25
3.2.1 Reducing the Average Number of Sensing Bits 26
3.2.1.1 The Two-Step Detection Method 27
3.2.1.2 Performance Analysis of the Two-Step Detection Method 29
3.2.2 Improving the Detection Performance 37
3.2.3 Improving Detection Performance and Average Sensing Bits 42
3.2.4 Simplifying the Calculation of Detection Threshold 44

Chapter 4 Simulation Results 52

Chapter 5 Conclusions 60

Bibliography 62

[1]Federal Communications Commission (FCC), “Spectrum policy task force report,” Rep. ET Docket no. 02-155, Nov. 2002.
[2]M. A. McHenry, “NSF spectrum occupancy measurements project summary,” Shared Spectrum Company Rep., Aug. 2005.
[3]M. McHenry, E. Livsics, T. Nguyen, and N. Majumdar, “XG dynamic spectrum access field test results,” IEEE Commun. Mag., vol. 45, pp. 51-57, Jun. 2007.
[4]J. Mitola, “Cognitive radio: An integrated agent architecture for software defined radio,” Doctor of Technology, Royal Inst. Technol. (KTH), Stockholm, Sweden, 2000.
[5]S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, pp. 201-220, Feb. 2005.
[6]H. Kim and K. Shin, “Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks,” IEEE Trans. Mobile Comput., vol. 7, no. 5, pp. 533–545, May 2008.
[7]Y.-C. Liang, Y. Zeng, E. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1326–1337, Apr. 2008.
[8]G. Turin, “Minimax strategies for matched-filter detection,” IEEE Trans. Commun., vol. 23, no.11, pp. 1370-1371, Nov. 1975.
[9]D. Cabric, S. M. Mishra, and R. Brodersen, ”Implementation issues in spectrum sensing for cognitive radios,” in Proc. 38th Asilomar Conf. Signals, Systems and Computers (ACSSC ’04), Pacific Grove, CA, Nov. 2004, pp. 772-776.
[10]A. Sahai, N. Hoven, and R. Tandra, “Some fundamental limits on cognitive radio,” in Proc. Allerton Conf. Communication, Control, and Computing, Oct. 2004, pp. 131–136.
[11]Z. Ye, J. Grosspietsch, and G. Memik, “Spectrum sensing using cyclostationary spectrum density for cognitive radios,” in Proc. IEEE Workshop Signal Process. Syst. (SIPS ’07), Shanghai, China, Oct. 2007, pp. 1–6.
[12]H. Urkowitz, “Energy detection of unknown deterministic signals,” Proc. IEEE, vol. 55, pp. 523-531, Apr. 1967.
[13]F. F. Digham, M.-S. Alouini and M. K. Simon, “On the energy detection of unknown signals over fading channels,” in Proc. of IEEE Int. Conf. Commun. (ICC ’03), Seattle, WA, May 2003, pp. 3575–3579.
[14]I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products, 5th ed. Academic Press, 1994.
[15]A. Nuttall, “Some integrals involving the QM function,” IEEE Trans. Inform. Theory, vol. 21, no. 1, pp. 95–96, Jan. 1975.
[16]R. Tandra and A. Sahai, “SNR walls for signal detection,” IEEE J. Select. Topics in Signal Processing, vol. 2, pp. 4–17, Feb. 2008.
[17]A. Ghasemi and E. S. Sousa, “Collaborative spectrum sensing for opportunistic access in fading environments,” in Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN ’05), Baltimore, MD, Nov. 2005, pp. 131-136.
[18]A. Ghasemi and E. S. Sousa, “Asymptotic performance of collaborative spectrum sensing under correlated log-normal shadowing,” IEEE Commun. Lett., vol. 11, no. 1, pp. 34-36, Jan. 2007.
[19]A. Ghasemi and E. S. Sousa, “Opportunistic spectrum access in fading channels through collaborative sensing,” J. Commun., vol. 2, no. 2, pp. 71–82, Mar. 2007.
[20]F. E. Visser, G. J. Janssen, and P. Pawelczak, “Multinode spectrum sensing based on energy detection for dynamic spectrum access,” IEEE Veh. Technol. Conf. (VTC ‘08), Singapore, May 2008, pp. 1394-1398.
[21]H. Uchiyama, K. Umebayashi, T. Fujii, F. Ono, K. Sakaguchi, Y. Kamiya and Y. Suzuki, “Study on Soft Detection Based Cooperative Sensing for Cognitive Radio Networks,” IEICE Trans. Commun., vol. E91-B, no. 1, pp. 95-101, Jan. 2008.
[22] K.-L. Hua, A Cooperative Spectrum Sensing Technique Based on the Water-Filling Principle for Cognitive Radio Systems, Master Thesis, Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan, Jul. 2009.
[23]Z. Quan, S. Cui, and A. H. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE J. Select. Topics in Signal Processing, vol. 2, no. 1, pp. 28-40, Feb. 2008.
[24]J. Ma, G. Zhao, and Y. Li, “Soft combination and detection for cooperative spectrum sensing in cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, no. 11, pp. 4502-4507 , Nov. 2008.
[25]B. Shen, L. Huang, C. Zhao, K. Kwak, and Z. Zhou, “Weighted cooperative spectrum sensing in cognitive radio networks,” in Proc. Int. Conf. Convergence and Hybrid Inform. Tech. (ICCIT ’08), Busan, Korea, Nov. 2008, pp. 1074-1079.
[26]Z. Quan, S. Cui, and A. H. Sayed, “An optimal strategy for cooperative spectrum sensing in cognitive radio networks,” in Proc. IEEE Global Telecommun. Conf. (GLOBECOM ‘07), Washington, D.C., Nov. 2007, pp. 2947–2951.
[27]J. Unnikrishnan and V. V. Veeravalli, “Cooperative Sensing for Primary Detection in Cognitive Radio,” IEEE J. Select. Topics in Signal Processing, vol. 2, no. 1, pp. 18–27, Feb 2008.
[28]R. V. Hogg and A. T. Craig, Introduction to mathematical Statistics, 4th edition. New York: Macmillan, 1978.
[29]J. Zhu, Z. Xu, F. Wang, B. Huang, and B. Zhang, ”Double threshold energy detection of cooperative spectrum sensing in cognitive radio,” in Proc. IEEE Int. Conf. Cognitive Radio Oriented Wireless Networks and Commun. (CROWNCOM ‘08), Singapore, May 2008, pp. 1-5.
[30]C. Sun, W. Zhang, and K. B. Letaief, “Cooperative spectrum sensing for cognitive radios under bandwidth constraints,” in Proc. IEEE Wireless Commun. And Networking Conf. (WCNC ‘07), Hong Kong, Mar. 2007, pp. 1–5.
[31]A. Jamshidi, “Performance analysis of low average reporting bits cognitive radio schemes in bandwidth constraint control channels,” IET Commun., vol. 3, pp. 1544-1556 , Jan. 2009.