多重輸入多重輸出系統(Multiple-input and multiple-output；MIMO)因為可以改善無線通訊的系統效能，大幅提升資料傳輸速率，雖然使用上會需要特定的設計而且有些問題需要克服，一直以來都是相當關鍵的無線通訊技術；另一方面，感知無線電(Cognitive radio；CR)使用頻譜感知的方式偵測並分配閒置的頻譜給其他使用者，也是相當熱門的研究題目，因為它有機會更進一步增進頻譜的使用效率；使用多個中繼端的合作式頻譜感知也可以提升感知無線電的效能。
本論文利用多天線技術來改善感知無線電並提出二階段合作式頻譜感知架構、位元映射對齊和幾種系統優化方法，其中為了符合提出的系統架構和感知無線電使用上的限制，只使用特定的幾種多天線演算法。波束合成可以加強能量偵測器的接收訊號；最大似然估計偵測法可以在決策整合時有效降低位元錯誤率；門檻值優化可以替能量偵測找出好的門檻值；決策對齊利用提出的位元映射對齊來避免在決策整合時高複雜度的位元估測；效能分析將使用數值模擬的方式來檢驗我們提出的系統架構所能改善的效果。

Multiple-input and multiple-output (MIMO) systems, which can increase the data rate significantly, have been a promising technology for improving the performance of wireless communication despite requiring specific designs and having technical problems. Cognitive radio (CR), which applies spectrum sensing to monitor and allocate idle frequency bands to secondary users is also a hot topic in researches when it comes to how to further improve the utilization efficiency of frequency bands. In addition, cooperative spectrum sensing which use multiple CR relays can improve performance for CR.
This dissertation utilizes multi-antenna technology to improve CR systems and proposes a two-stage cooperative spectrum sensing structure, a bit mapping alignment scheme and several optimization methods. Because of some constraints in CR, certain multi-antenna algorithms are selected to apply to the parts of the system structure of the CR networks. Beamforming reception enhances the strength of the received signal for energy detectors. Maximum likelihood detection reduces the bit error rate for decision fusion. Threshold optimization optimizes thresholds for energy detection. Decision alignment utilizes the proposed bit mapping alignment scheme to avoid complicated bit estimation for decision fusion. The performance evaluations are shown using numerical simulations to validate the effectiveness of the proposed system structures.

Chapter 1 Introduction 1
1.1 Background 1
1.2 Survey of references 3
1.3 Synopsis of the dissertation 4
Chapter 2 Introduction of Multi-Antenna Technology for Cognitive Radio 7
2.1 Introduction of MIMO systems 9
2.2 Beamforming 10
2.2.1 MUSIC (Multiple signal classification) 12
2.2.2 ESPRIT (Estimation of signal parameter via rotational techniques) 14
2.2.3 TLS ESPRIT (Total Least Squares ESPRIT) 17
2.3 Maximum likelihood 19
2.4 Alignment in multi-user MIMO 19
2.4.1 Interference processing 20
2.4.2 Degree of freedom 21
2.4.3 Bit mapping alignment 22
2.5 Introduction of cognitive radio 23
2.6 Introduction of cooperative spectrum sensing 25
2.6.1 Data fusion 26
2.6.2 Decision fusion 26
2.6.3 Decision optimization for decision fusion 28
Chapter 3 Two-stage cooperative spectrum sensing structure 29
3.1 Stage 1: energy detector with data fusion 30
3.2 Stage 2: decision fusion 33
3.3 Rounding down method 33
3.4 Threshold optimization 34
3.5 Decision alignment 36
Chapter 4 Simulation Results 41
4.1 The performance evaluation of the two-stage cooperative spectrum sensing structure 42
4.1.1 Stage 1: energy detector with data fusion 42
4.1.2 Demodulation of decision bits using MLD in a MIMO system 48
4.1.3 Decision optimization for the two-stage cooperative spectrum sensing structure 50
4.2 The performance evaluation of rounding down method for the two-stage cooperative spectrum sensing structure 53
4.2.1 Relation between the standard deviation and the rounding down method 55
4.2.2 Statistics of rounding down method 59
4.3 The performance evaluation of threshold optimization 63
4.3.1 Optimizing the threshold at an energy detector 63
4.3.2 Threshold optimization for the two-stage cooperative spectrum sensing structure 76
4.4 The performance evaluation of decision alignment 85
Chapter 5 Conclusion 89

[1] M. A. McHenry, “NSF spectrum occupancy measurements project summary,” Shared Spectrum Company, August 2005.
[2] L. Khalid, and A. Anpalagan, “Emerging cognitive radio technology: Principles, challenges and opportunities,” Computers & Electrical Engineering, vol. 36, no. 2, pp. 358-366, March 2010.
[3] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, February 2005.
[4] T. Yucek, and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 116-130, March 2009.
[5] D. Cabric, A. Tkachenko, and R. Brodersen, “Spectrum sensing measurements of pilot, energy and collaborative detection,” IEEE Military Commun. Conf., Washington, D.C., USA, pp, 1-7, 2006.
[6] A. Sahai, N. Hoven, and R. Tandra, “Some fundamental limits on cognitive radio,” 42nd Allerton Conf. Commun., Control, and Comput., October 2004.
[7] Weifang Wang, “Spectrum Sensing for Cognitive Radio,” The third International Symposium on Intelligent Information Technology Applications Workshop (IITAW), November 2009.
[8] P. D. Sutton, K. E. Nolan, and L. E. Doyle, “Cyclostationary signatures in practical cognitive radio applications,” IEEE J. Sel. Areas Commun., vol. 26, no. 1, pp. 13–24, January 2008.
[9] F. F. Digham, M.-S. Alouini, and M. K. Simon, “On the energy detection of unkown signals over fading channels,” IEEE ICC, pp. 3575–3579, 2003.
[10] F. F. Digham, M.-S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fading channels,” IEEE Transactions on Communications, vol. 55, no. 1, pp. 21 –24, 2007.
[11] S. Atapattu, C. Tellambura and H. Jiang, “Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks,” IEEE Transactions on Wireless Communications, vol. 10, no. 4, pp. 1232-1241, April 2011.
[12] H. Sun, A. Nallanathan, J. Jiang and C. X. Wang, “Cooperative spectrum sensing with diversity reception in cognitive radios,” 2011 6th International ICST Conference on Communications and Networking in China (CHINACOM), Harbin, pp. 216-220, 2011.
[13] Z. Quan, S. Cui, and A. H. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE J. Sel. Topics Signal Process., vol. 2, no. 1, pp. 28-40, February 2008.
[14] G. Xiong and S. Kishore, “Cooperative Spectrum Sensing with Beamforming in Cognitive Radio Networks,” IEEE Communications Letters, vol. 15, no. 2, pp. 220-222, February 2011.
[15] S. Atapattu, C. Tellambura, and H. Jiang, “Energy detection based cooperative spectrum sensing in cognitive radio networks,” IEEE Trans. Wirel. Commun., 10(4):1232–1241, April 2011.
[16] W. Zhang, R. Mallik, and K. Letaief, “Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 8, no. 12, pp. 5761–5766, December 2009.
[17] Bai, Z, Gao, P, Su, Y, Han, T, Kwak, K, Park, D, “An improved cooperative spectrum sensing of femtocells with hybrid TDD access scheme in massive MIMO system,” Int J Commun Syst., vol. 30, issue 9, pp. e3199, October 2017. https://doi.org/10.1002/dac.3199
[18] R. O. Schmidt, “Multiple Emitter Location and Signal Parameter Estimation,” IEEE Transactions on Antenna and Propagation, vol. AP-34, No.3, pp. 276-280, March 1986.
[19] F. Yan, M. Jin and X. Qiao, “Low-Complexity DOA Estimation Based on Compressed MUSIC and Its Performance Analysis,” IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 1915-1930, April 15, 2013.
[20] R. Roy and T. Kailath, “ESPRIT-Estimation of Signal Parameters via Rotational Invariance Techniques,” IEEE Transactions on Acoustics, speech, and Signal Processing, vol. ASSP-37, pp. 984-995, July 1989.
[21] B. Ottersten, Mats Viberg and Thomas Kailath, “Performance Analysis of the Total Least Squares ESPRIT Algorithm,” IEEE Transaction on Signal Processing, vol. 39, No. 5, pp. 1122-1135, May 1991.
[22] X. Zhang and D. Xu, “Low-complexity ESPRIT-based DOA estimation for colocated MIMO radar using reduced-dimension transformation,” Electronics Letters, vol. 47, no. 4, pp. 283-284, February 17, 2011.
[23] Jingming Wang; Daneshrad, B., “A comparative study of MIMO detection algorithms for wideband spatial multiplexing systems,” IEEE Wireless Communications and Networking Conference, vol. 1, pp.408-413, March 2005.
[24] Te Han; Kobayashi, K., “A new achievable rate region for the interference channel,” IEEE Transactions on Information Theory, vol. 27, no.1, pp.49,60, January 1981.
[25] Host-Madsen, A; Nosratinia, A, “The multiplexing gain of wireless networks,” International Symposium on Information Theory, pp.2065,2069, September 4-9, 2005.
[26] J. Mitola, and G. Q. Maguire Jr., “Cognitive radio: making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13-18, August 1999.
[27] J. Mitola III, “Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio”, Ph. D. diss., Royal Institute of Technology, Sweden, May 8, 2000.
[28] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, February 2005.
[29] M. Subhedar, and G. Birajdar, “Spectrum sensing techniques in cognitive radio networks: A survey,’’ International Journal of Next-Generation Networks (IJNGN), vol. 3, no. 2, June 2011.
[30] P. D. Sutton, K. E. Nolan, and L. E. Doyle, “Cyclostationary signatures in practical cognitive radio applications,” IEEE J. Sel. Areas Commun., vol. 26, no. 1, pp. 13–24, January 2008.
[31] G. Ganesan and Y. Li, “Cooperative spectrum sensing in cognitive radio, part I: two user networks,” IEEE Trans. Wireless Commun., vol. 6, no. 6, pp. 2204–2213, June 2007.
[32] R. Fan and H. Jiang, “Optimal multi-channel cooperative sensing in cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 9, no. 3, pp. 1128–1138, March 2010.
[33] W. Zhang and K. B. Letaief, “Cooperative communications for cognitive radio networks,” Proc. IEEE, vol. 97, no. 5, pp. 878–893, May 2009.
[34] J. N. Laneman, D. N. C. Tse, and G. W. Wornell, “Cooperative diversity in wireless networks: efficient protocols and outage behavior,” IEEE Trans. Inf. Theory, vol. 50, no. 12, pp. 3062–3080, December 2004.
[35] Bilgehan B, Abdelbari A. Fast detection and DOA estimation of the unknown wideband signal sources. Int J Commun Syst., vol. 32, issue 11, pp. e3968, July 2019. https://doi.org/10.1002/dac.3968
[36] S. Atapattu, C. Tellambura, and H. Jiang, “Energy detection based cooperative spectrum sensing in cognitive radio networks,” IEEE Trans. Wirel. Commun., 10(4):1232–1241, April 2011.
[37] A. Ghasemi and E. Sousa, “Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments,” First IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005.