在感知無線電(Cognitive Radio, CR)技術中，若已偵測到主系統(Primary System)所使用的頻段，感知使用者(Cognitive User)必須停止在該頻段傳輸抑或減少在該頻段的發射功率。對於正交分頻多工(Orthogonal Frequency Division Multiplexing , OFDM)感知無線電系統架構而言，Active Interference Cancellation (AIC)這個干擾抑制技術是一個熱門的選擇。此技術的概念是犧牲些許數目的子載波，並對這些子載波的調變做調整，使其能消除其他子載波對主系統造成的干擾。AIC技術採用的是最小平方(Least Squares, LS)法，而本論文的重點在於降低此技術的運算複雜度以及增進此技術的干擾抑制效能。
降低運算複雜度方面主要是基於以下兩個研究發現：1)在最小平方解(LS Solution)中，許多矩陣的數值只取決於主系統佔用的頻寬大小，而與該頻段的位置無關；2) 最小平方法可求得那些被用來消除干擾的子載波所應該攜帶的數據，而這些數據之間的統計關係(Statistical Relation)則可用來大幅降低最小平方解的複雜度。在增進干擾抑制效能方面，原本的效能指標(Performance Metric)是運用最小平方法將主系統頻段上的總殘餘干擾功率最小化，但會有下述的不良狀況：雖然總殘餘功率被降至最低，但有可能部份頻段卻遭受嚴重干擾。因此，最佳的效能指標是將主系統頻段上的“最大”殘餘干擾功率最小化。
無論是原本的抑或改良過的AIC技術，運用在超寬頻(Ultra-Wideband, UWB)感知無線電系統時，都會面臨頻譜過衝問題(Spectrum Overshoot Problem)。因此，本論文中提出的演算法，在AIC技術的最佳化問題中附加功率限制，使其有抵抗頻譜過衝的能力。最後，模擬結果證實，降低運算複雜度所導致的效能損失微乎其微；新採用的效能指標可增進效能，同樣是抑制干擾到低於某一水平，新的效能指標能夠大幅減少所需犧牲的子載波數目。

In cognitive radio (CR), a cognitive user must cease transmission or reduce the transmit power at the band where a primary system (PS) is detected. To this end, a popular method for orthogonal frequency division multiplexing (OFDM) based CR is the active interference cancellation (AIC) which tunes the modulation of some subcarriers to minimize the interference to PS. This thesis focuses on complexity reduction and performance enhancement of AIC. The former relies on the findings that i) many matrices in AIC depend only on the PS bandwidth and are irrelevant to where PS resides, and ii) statistical relation among the data carried by subcarriers for interference suppression is available. For the latter, performance metric of AIC that minimizes the total residual power at the PS band is adjusted to minimizing the largest residual power, avoiding the undesirable situation that, although the total power is the least, part of the band is severely interfered. Spectrum overshoot problem occurred in an ultra-wideband (UWB)-CR system is also addressed. To this end, algorithms (constrained AIC-based methods) free from spectrum overshoot have been designed. Simulation results demonstrate that complexity reduction of AIC induces negligible performance loss, and the new performance metric significantly reduces the number of subcarriers required to suppress the interference below a certain level.

[1] J. Mitola, “Cognitive radio: making software radio more personal,” IEEE Pers. Commun., vol. 6, no. 4, pp. 48-52, 1999.
[2] J. Mitola, Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio, Ph.D. dissertation, Royal Institute of Technology (KTH), Sweden, May 2002.
[3] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, Feb. 2005.
[4] T. Weiss, J. Hillenbrand, A. Krohn, and F. K. Jondral, “Mutual interference in OFDM-based spectrum pooling systems,” in IEEE Vehicular Technology Conf., May 2004.
[5] I. Cosovic, S. Brandes, and M. Schnell, “Subcarrier weighting: a method for sidelobe suppression in OFDM systems,” IEEE Commun. Lett., vol. 10, no. 6, pp. 444-446, June 2006.
[6] I. Cosovic and T. Mazzoni, “Suppression of sidelobes in OFDM systems by multiple-choice sequences,” European Trans. on Telecommun., vol. 17, no. 6, pp. 623-630, Dec. 2006.
[7] H. A. Mahmoud and H. Arslan, “Sidelobe suppression in OFDM-based spectrum sharing systems using adaptive symbol transition,” IEEE Commun. Lett., vol. 12, no. 2, pp. 133-135, Feb. 2008.
[8] H. Yamaguchi, “Active interference cancellation technique for MB-OFDM cognitive radio,” in 34th European Microwave Conf., 2004, vol. 2, pp. 1105-1108.
[9] S. Brandes, I. Cosovic, and M. Schnell, “Reduction of out-of-band radiation in OFDM systems by insertion of cancellation carriers,” IEEE Commun. Lett., vol. 10, no. 6, pp. 420-422, June 2006.
[10] S. M. Mishra, S. ten Brink, R. Mahadevappa, and R. W. Brodersen, “Detect and avoid: an ultra-wideband/WiMax coexistence mechanism,” IEEE Communications Magazine, vol. 45, no. 6, pp. 68-75, June 2007.
[11] C. L. Lawson, Contributions to the Theory of Linear Least Maximum Approximations, Ph.D. dissertation, University of California, Los Angeles, 1961.
[12] FCC Notice of Proposed Rule Making, Revision of Part 15 of the Commission's Rules Regarding Ultrawideband Transmission Systems, Federal Communications Commission, Washington, DC, ET-Docket 98-153.
[13] A. R. S. Bahai, B. R. Saltzberg, and M. Ergen, Multi-Carrier Digital Communications: Theory and Applications of OFDM, Kluwer Academic/Plenum, 1999.
[14] S. M. Kay, Fundamentals of Statistical Signal Procssing: Estimation Theory, vol. 1, Prentice Hall PTR, 1993.
[15] S.-G. Huang and C.-H. Hwang, “Low complexity active interference cancellation for OFDM cognitive radios,” in IEEE Wireless Communications & Networking Conf., March/April 2008.
[16] S. Ellacott and Jack Williams, “Linear Chebyshev approximation in the complex plane using Lawson's algorithm,” Math. Comp., vol. 30, no. 133, pp. 35-44, 1976.
[17] Jd. P. Pav¶on, Sai Shankar N, V. Gaddam, K. Challapali, and Chun-Ting Chou, “The MBOA-WiMedia speci‾cation for ultra wideband distributed networks,” IEEE Communications Magazine, vol. 44, no. 6, pp. 128-134, June 2006.
[18] Mokhtar S. Bazaraa, Hanif D. Sherali, and C. M. Shetty, Nonlinear Programming: Theory and Algorithms, Wiley-Interscience, 3rd edition, 2006.
[19] Gene H. Golub and Charles F. Van Loan, Matrix Computations, Johns Hopkins University Press, Baltimore, 3rd edition, 1996.
[20] K. C. Toh, M. J. Todd, and R. H. Tutuncu, “SDPT3|a Matlab software package for semide‾nite programming,” Optimization Methods and Software, vol. 11, pp. 545-581, 1999.
[21] R. H. Tutuncu, K. C. Toh, and M. J. Todd, “Solving semide‾nite-quadratic-linear programs using SDPT3,” Mathematical Programming Ser. B, vol. 95, pp. 189-217, 2003.
[22] L. Sanguinetti, A. A. D'Amico, and I. Cosovic, “On the performance of cancellation carrier-based schemes for sidelobe suppression in OFDM networks,” in IEEE Vehicular Technology Conf., May 2008.
[23] C. Ciochina, F. Buda, and H. Sari, “An analysis of OFDM peak power reduction techniques for WiMAX systems,” in IEEE International Conference on Communications (ICC'06), June 2006.