近年來由於無線通訊技術的蓬勃發展，無線網路之使用者已越來越多，頻譜的不足儼然成為一個重要的問題。FCC研究發現，傳統的頻譜分配方式，其頻譜使用率幾乎都在25%以下。感知無線電技術具有一定的智慧，可以感測週遭的環境，判斷閒置的頻譜，並且動態的選擇空閒的頻譜進行傳輸和學習；該技術之主要概念，是當主要使用者(PUs)沒有使用其頻譜時，次要使用者(SUs)可以使用，但必須要滿足主使用者之限制；而系統要如何將這些閒置頻譜快速且適當地分配給次要使用者，達到頻譜使用率和通道容量之最佳化，是研究感知無線電技術的主要方向。
在多用戶OFDM感知無線電之下我們提出了一個高效率功率分配演算法。一開始我們在只考慮在由干擾溫度限制求出的子載波功率條件之下做拉格朗日乘數法，並且藉由此法來求出滿足這個條件下之次要使用者的全部功率，然後我們再將這個資訊與原來次要使用者可以使用的全部功率，作結合取其最小值來當作新的次要使用者可以使用的全部功率；其次我們考慮在次要使用者的上傳功率限制下做拉格朗日乘數法，並且藉由此法來求出每個子載波最適當的傳送功率，來達到整個系統容量的最大值；此外經由模擬結果的驗證，我們所提出的方法與其他相關文獻的方法比較之下，我們的效能相當不錯，並且計算複雜度不會太高，相當適合用在多用戶OFDM感知無線電的頻譜分享上。

As the vigorous development of wireless communications technology, there are more and more users accessing the wireless networks, and the spectrum scarcity becomes a severe problem, especially when the high data throughput is required. It was indicated by the Federal Communications Commission (FCC) of the United States that the utilization efficiency is less than 30% of the allocated spectrum in the country. Cognitive radio (CR) is a technology that can enable an intelligent device to sense its surrounding environment, identify idle spectrums, and use them for data transmission dynamically, thereby increasing the spectrum utilization. The main idea of CR is that the secondary users (SUs) can use idle spectrums of the primary users (PUs), but they must satisfy the transmitted power constraints for avoiding interference to PUs. One of the most significant studies in CR is how to quickly allocate idle spectrums appropriately to SUs for maximizing the spectrum efficiency as well as the system capacity.
In this thesis, we propose an efficient power allocation algorithm for spectrum sharing in multiuser orthogonal frequency division multiplexing (OFDM) based CR systems. The proposed algorithm is derived by using the Lagrange multipliers to solve the optimization problem subject to 1) the power constraint on each subcarrier of SUs represented by the interference temperature limit (ITL) of PUs and 2) the total power constraint on each SU. As compared to related works for spectrum sharing, the proposed approach achieves a higher system capacity with acceptable computational complexity.

[1] J. Z. Sun, J. Sauvola, and D. Howie, “Features in future: 4G visions from a technical perspective,” in Proc. 2001 IEEE Global Telecommun. Conf. (GLOBECOM ’01), San Antonio, TX, Nov. 2001, pp. 3533-3537.
[2] Federal Communications commission (FCC), “Spectrum Policy Task Force,” ET Docket no. 02-135, Nov. 15, 2002.
[3] J. Mitola III and G. Q. Maguire, Jr.,“Cognitive radio: making software radios more personal,”IEEE Pers. Commun., vol. 6, no. 4, pp. 13-18, Aug. 1999.
[4] F. F. Digham, “Joint power and channel allocation for cognitive radios,” in Proc. 2008 IEEE Wireless Commun. and Networking Conf. (WCNC’08), Las Vegas, NV, March 31-April 3 2008, pp. 882-885.
[5] T. Yucek and H. Arslan,“A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Trans. Commun. Surveys Tuts., vol. 11, no. 1, pp. 116-130, Jan. 2009.
[6] M. Haddad, A. M. Hayar, G. E. Oien, and S. G. Kiani, “Uplink distributed binary power allocation for cognitive radio networks,” in Proc. 2008 IEEE Cognitive Radio Oriented Wireless Network and Comm. (CrownCom’08), Singapore, May 2008, pp. 1-4.
[7] R. Zhang and Y.-C. Liang, “Exploiting multi-antennas for opportunistic spectrum sharing in cognitive radio networks,” IEEE Trans. Signal Process., vol. 2, no. 1, pp. 88-102, Feb. 2008.
[8] R. van Nee and R. Prasad, OFDM for Wireless Multimedia Communications. Boston/London: Artech House, 1999.
[9] IEEE 802.11a Standard, ISO/IEC 8802-11:1999/Amd 1:2000(E).
[10] IEEE 802.11g Standard, Further Higher-Speed Physical Layer Extension in the 2.4 GHz Band.
[11] E. Lawrey,“Multiuser OFDM,”in Proc. 2009 IEEE Int. Symp. Signal Processing and Its Applications (ISSPA’09), Brisbane, Australia, Aug. 1999, pp. 761-764.
[12] S. Sesia, I. Toufik, and M. Baker, LTE-The UMTS Long Term Evolution: From Theory to Practice. Chichester/U.K.: John Wiley & Sons, 2009.
[13] T. C. Clancy, “Formalizing the Interference Temperature Model,” WILEY J. Wireless Commun. and Mobile Comput., vol. 7, pp. 1-8, Nov. 2007.
[14] M. A. McHenry, “NSF spectrum occupancy measurements project summary,” shared spectrum co. report, Aug. 2005.
[15] A. Ghasemi and E. S. Sousa,“Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs,”IEEE Commun. Mag., vol. 46, no. 4, pp. 32-39, Apr. 2008.
[16] I. Kim, H. L. Lee, B. Kim, and Y. H. Lee,“On the user of linear programming for dynamic subchannel and bit allocation in multiuser OFDM,”in Proc. 2001 IEEE Global Telecommun. Conf. (GLOBECOM ’01), San Antonio, TX, Nov. 2001, pp. 3648-3652.
[17] C. Y. Wang, R. S. Cheng, K. B. Letaief, and R. D. Murch,“Multicarrier OFDM with adaptive subcarrier, bit, and power allocation,”IEEE J. Select. Areas Commun., vol. 17, no. 10, pp. 1747-1758, Oct. 1999.
[18] J. Jang and K. B. Lee,“Transmit power adaptation for multiuser OFDM systems,”IEEE J. Select. Areas Commun., vol. 21, no. 2, pp. 171-178, Feb. 2003.
[19] IEEE 802.22 Working Group on Wireless Regional Area Networks, www.ieee802.org/22.
[20] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge University Press, 2004.
[21] P. Wang, M. Zhao, L. Xiao, S. Zhou, and J. Wang, “Power allocation in OFDM-based cognitive radio systems,” in Proc. 2007 IEEE Global Telecommun. Conf. (GLOBECOM ’07), Washionton, DC, USA, Nov. 2007, pp. 4061-4065.
[22] G. L. Stüber, Principles of Mobile Communication. 2nd ed., Norwell, MA: Kluwer Academic Publishers, 2002.