由於正交分頻多工系統(OFDM)在多路徑環境之下十分穩定，且在頻域等化的複雜度低，使得OFDM經常被應用於無線通訊的傳輸上。為了達到更高的資料傳輸量與頻寬效益，我們可使用盲閉式(Blind)偵測來取代原來的引導訊號(Pilot Signal)偵測。
本篇論文我們考慮符元正規化之盲閉式OFDM訊號偵測。使用通道脈衝響應(CIR)來模擬OFDM系統中各子頻道的通道響應(CR)，再加上使用branch-and-bound 原理，來解決非線性的整數規劃問題。為了更進一步的減少系統複雜度，我們在所提出的方法中對其他的符元做振幅正規化。同時也利用多項式來近似每個子通道的時變通道響應。接著，可利用預先估計的通道響應，來找尋較佳的起始資料串，此資料串用來做為 branch-and-bound 演算法的初始值，可大量地減少所需的拜訪點數。模擬結果證明使用本篇所提出的演算法可以有效地降低樹狀搜尋的複雜度。

For wireless communications, the orthogonal-frequency division multiplexing (OFDM) system is widely applied due to its robustness to the multipath environment and its low-complexity frequency-domain equalization. In order to increase the data throughput, we can use the blind method instead of the pilot-assisted method for the signal detection in OFDM systems.
In this thesis, we propose a blind detection algorithm for the OFDM system with normalized symbol amplitudes. We use channel impulse response (CIR) to model the channel response (CR) variation across sub-channels. Then we apply the branch-and-bound principle to solve the non-linear integer programming problem. We first consider the blind detection algorithm that only normalizes the last symbol, and then we further consider the normalization of other symbols to further reduce the complexity. We also model the time-variation of CRs in each sub-channel as a polynomial, and use the predicted CRs to obtain a better initial data sequence in the tree search process. A better initial data sequence can further reduce the number of visited nodes and system complexity. Simulation results validate that the proposed blind detection algorithm can effectively reduce the complexity while maintain the error rates.

[1]J. A. C. Bingham, “Multicarrier modulation for data transmission: An idea whose time has come,” IEEE Commun. Mag., vol. 28, pp. 5-14, May 1990.
[2]O. Edfors, M. Sandell, and J.-J. van de Beek, “OFDM channel estimation by singular value decomposition,” IEEE Trans. Commun., vol. 46, pp. 931-939, July 1998.
[3]Y. Li , “Pilot-symbol-aided channel estimation for OFDM in wireless systems,” IEEE Trans. Veh. Technol., vol. 49, pp. 1207-1215, Jul. 2000.
[4]M.-X. Chang and Y. T. Su, “Model-Based channel estimation for equalizing OFDM signals,” IEEE Trans. Commun., vol. 50, pp. 540-544, Apr. 2002.
[5]F.-C. Zheng, S. McLaughlin, and B. Mulgrew, “Blind equalization of nonminimum phase channels: High order cumulant based algorithm,” IEEE Trans. Signal Process, vol. 41, pp. 681-691, Feb. 1993.
[6]H. H. Zeng and L. Tong, “Blind channel estimation using the second-order statistics: Asymptotic performance and limitations,” IEEE Trans. Signal Process, vol. 45, pp. 2060-2071, Aug. 1997.
[7]X. G. Doukopoulos and G. V. Moustakides, “Blind adaptive channel estimation in OFDM systems,” IEEE Trans. Wireless. Commun., vol. 5, pp. 1716-1735, Jul. 2006.
[8]S. A Banani and R. G. Vaughan, “OFDM with iterative blind channel estimation,” IEEE Trans. Veh. Technol., vol. 59, pp. 4298-4308, Nov. 2010.
[9]M.-X. Chang and Y. T. Su, “Blind and semiblind detections of OFDM signals in fading channels,” IEEE Trans. Commun., vol. 52, pp. 744-754, May. 2004.
[10]J.-H. Chen and M.-X. Chang, “Blind and semiblind detection of OFDM signals in the Multipath Fading Channel,” in Proc. IEEE GlobeCom Workshops, 2011.
[11]H. A. Taha, Integer Programming, Theory, Applications, and Computations, New York: Academic Press, 1975.
[12]Y. Li, and X. Huang, “The Simulation of Independent Rayleigh Faders,” IEEE Trans. Commun., vol.50, NO. 9, pp. 1503-1514, Sep. 2002.