感知無線電(cognitive radio)是基於提升頻譜使用效率所發展出的一項新興技術，其具備了有效解決頻譜使用不足之能力。為了提升頻譜的使用效率，次要使用者(secondary user)被授權得在主要使用者(primary user)不存在的情況下借用其頻帶來傳送資料。為了保障主要使用者的權利，使其不受到次要使用者的干擾，次要使用者必須在傳送資料之前透過頻譜感測(spectrum sensing)探知主要使用者的動態並決定其資料傳送與否。因此，可靠的頻譜感測技術對於感知無線電系統是相當重要的。
在此篇論文中，考量到較低的運算複雜度，我們選擇使用能量偵測器(energy detector)來偵測頻譜。傳統的能量偵測器使用固定的判定門檻值(decision threshold)來判斷主要使用者存在與否。當判定門檻值較高時，會得到較低的偵測機率(detection probability)以及錯誤警報機率(false-alarm probability)，進而可得相對應較高的次要使用者使用頻譜之機率；反之，當判定門檻值較低時，會得到較高的偵測和錯誤警報機率，進而使得次要使用者使用頻譜的機率降低。在此篇論文中，我們依據次要使用者傳送端的佇列長度(queue length)來調整判定門檻值，針對次要使用者使用頻譜的機率，將頻譜感測和排程技術(scheduling)結合在一起。另一方面，為了滿足對主要使用者之保護，我們會動態調整次要使用者的傳輸功率，使得主要使用者的中斷機率(Outage probability)維持在容許範圍內。
我們建立了一個封包的傳遞模型來探討封包延遲，並提出了佇列長度和判定門檻值之間的關係函數，用來控制判定門檻值的調整，藉以最佳化封包延遲的現象。根據模擬結果可以發現，我們所提出的機制相較於傳統的能量偵測機制，在封包延遲和封包遺失率表現上都有明顯的改善。

Cognitive radio (CR) is an emerging communication technique which can enhance the spectrum-usage efficiency by allowing some secondary users (SUs) to operate over the licensed spectrum as long as a primary user (PU) is inactive. To sufficiently protect PU communications from interference imposed by SUs, a CR system requires reliable spectrum sensing to determine whether an SU is allowed to access the licensed spectrum or not. However, such an opportunistic spectrum access mechanism would lead to serious latency and buffer overflow problems.
In this thesis, we propose a low-latency joint spectrum sensing and scheduling scheme based on an energy detector for opportunistic SU transmission. Unlike the conventional energy detector using a fixed threshold to detect the PU’s occurrence, the proposed scheme dynamically regulates the detection threshold according to the queue length at the SU transmitter, where the SU transmission power is adjusted properly to confine the PU’s outage probability at an acceptable level and two linear policy functions are adopted for the queue delay minimization. Numerical results indicate that the proposed approach has significant improvements over the conventional energy detection scheme with a fixed threshold in terms of the queue delay of SU transmission and the packet loss due to buffer overflow.

[1] Federal Communications Commission, “Spectrum policy task force report,” Rep. ET Docket no. 02-155, Nov. 2002.
[2] J. Mitola and G. Q. Maguire, “Cognitive radios: making software radios more personal,” IEEE Personal Commun., vol. 6, no. 4, pp. 13-18, Aug. 1999.
[3] J. Mitola, “Cognitive radio: An integrated agent architecture for software defined radio,” Doctor of Technology, Royal Inst. Technol. (KTH), Stockholm, Sweden, 2000.
[4] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, pp. 201-220, Feb. 2005.
[5] H. Jonathan Chao and X. Guo, Quality of Service Control in High-Speed Networks, New York: Wiley, 2002.
[6] Z. Wang, Internet QoS: Architectures and Mechanisms for Quality of Service, San Francisco: Morgan Kaufmann, 2001.
[7] F. F. Digham, M. S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fading channels,” in Proc. 2002 IEEE Int. Conf. Commun. (ICC 2003), pp. 3575-3579, May 2003.
[8] J. Hillenbrand, T. A. Weiss, and F. Jondral, “Calculation of detection and false alarm probabilities in spectrum pooling systems” IEEE Commun. Lett., vol. 9, no. 4, pp. 349-351, Apr. 2005.
[9] H. Urkowitz, “Energy detection of unknown deterministic signals,” Proc. IEEE, vol. 55, pp. 523-531, Apr. 1967.
[10] Z. Ye, G. Memilk, and J. Grosspietsch, “Energy detection using estimated noise variance for spectrum sensing in cognitive radio networks,” in Proc. 2008 IEEE Wireless Commun. And Networking Conf. (WCNC 2008), pp. 711-716, Mar. 2009.
[11] F.F. Digham, M.-S. Alouini and M. K. Simon, “On the energy detection of unknown signals over fading channels,” IEEE Trans. Commun., vol.55, no.1, pp. 3575-3579, Jan. 2007.
[12] A. Ghasemi and E. S. Sousa, “Collaborative spectrum sensing for opportunistic access in fading environments,” in proc. IEEE DySPAN 2005, Baltimore, MD, USA, pp. 131-136, Nov. 2005.
[13] I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products, 6th ed. San Diego, CA: Academic, 2000.
[14] Z. Bao, B. Wu, P. Ho, and X. Liang, “Adaptive threshold control for energy detection based spectrum sensing in cognitive radio networks,” in Proc. IEEE Conf. on Global Commun. Dec. 2011.
[15] Q. Du and X. Zhan. “Queue-aware spectrum sensing for interference-constrained transmissions in cognitive radio networks,” in proc. IEEE Int. Conf. Commun. (ICC 2010).
[16] C.-S. Chang, Performance Guarantees in Communication Networks, Springer-Verlag London, 2000.
[17] D. Wu and R. Negi, “Effective capacity: A wireless link model for support of quality of sevice,” IEEE Trans. on Wireless Commun., vol. 2, no. 4, pp. 630-643, July 2003.
[18] F. Chapeau-Blondeau and A. Monir, “Numerical evaluation of the Lambert W function and application to generation of generalized Gaussian noise with exponent 1/2,” IEEE Trans. On Signal Processing, vol. 50, no. 9, pp. 2160-2165, Sep. 2002.
[19] W. Chen, K. B. Letaief, and Z. Cao, “A joint coding and scheduling method for delay optimal cognitive multiple access,” in Proc. IEEE Int. Conf. Commun. (ICC), pp. 3558-3562, May 2008.
[20] D. Hamza, S. Aissa, “An optimal probabilistic multiple-access scheme for cognitive radios,” IEEE Trans. on Veh. Technology, vol. 61, no. 7, Sep. 2012
[21] A. Makowski, B. Melamed, and W. Whitt, “On averages seen by arrivals in discrete time,” in Proc. IEEE Conf. Decision Control, tampa, FL, vol. 2, pp. 1084-1086, Dec. 1989.
[22] B. Kim, Y. Chang, Y. C. Kim, and B. D. Choi, “A queueing system with discrete autoregressive arrivals,” Performance Eval., vol. 64, no. 2, pp. 148-161, Feb. 2007.