為了在通道估測時不受資料序列的干擾影響，在資料相關性疊加訓練 (Data-Dependent Superimposed Training, DDST)系統中，資料序列在傳送端加上 訓練序列之前會先移除資料的循環平均值。接收端在加入了這干擾項之後會造成 資料判斷問題(Data Identification Problem, DIP)。 本論文中，我們基於之前的論文提出了兩個新的預編碼架構。為了維持低的峰均值功率比(Peak-to-Average Power Ratio, PAPR),我們將預編碼矩陣限制為對角矩陣。第一個架構藉由提升兩個最接近的碼字之距離來提升效能。為了確保我們提出的預編碼架構在M階相移鍵控(M-ary Phase Shift Keying, MPSK)和多重陣列正交振幅調變(M-ary Quadrature Amplitude Modulation, MQAM)中是有效率的，在論文中列出了一些需要滿足的條件。第二個架構則是為了追求接收端的低複雜度所提出的。這兩個架構分別對位元錯誤率(Bit Error Rate, BER)和複雜度有所取捨。最後，模擬結果顯示出峰均值功率比在我們所提出的方法中有明顯的改善，並且能有效的解決資料辨別問題。

For channel estimation without data-induced interference in data-dependent superimposed training (DDST) scheme, the data sequence is shifted by subtracting a data-dependent sequence before added to training sequence at transmitter. The distorted term causes the data identification problem (DIP) at the receiver. In this thesis, we propose two precoding schemes based on previous work. To maintain low peak-to-average power ratio (PAPR), the precoding matrix is restricted to a diagonal matrix. The first scheme is proposed to enlarge the minimum distance between the closest codewords, termed as efficient diagonal scheme. Conditions to make sure the precoding matrix is efficient for M-ary phase shift keying (MPSK) and M-ary quadrature amplitude modulation (MQAM) modulation are listed in this paper. The second scheme pursues a lowest complexity at receiver which means the amount of searching set is reduced. It is a trade-off between the better bit error rate (BER) performance and a lower complexity at
receiver. The simulation results show that PAPR have been improved and the DIP is solved in both schemes.

論文審定書 ....................................................................................................................... i
誌謝 .................................................................................................................................. ii
中文摘要 ......................................................................................................................... iii
Abstract .......................................................................................................................... iv
Chapter 1 Introduction .................................................................................................. 1
Chapter 2 System Model ................................................................................................ 5
2.1 Traditional SC-FDE System ............................................................................... 5
2.2 DDST Scheme .................................................................................................... 6
Chapter 3 Previous Literatures ................................................................................... 13
3.1 Iterative Symbol-by-Symbol Detection Algorithm .......................................... 13
3.2 Infinite Constellation Shift Algorithm .............................................................. 14
3.3 Gradient Infinite Constellation Shift Algorithm ............................................... 19
3.4 Precoding Matrix with Perfect Sequence ......................................................... 20
Chapter 4 Proposed Method ........................................................................................ 22
4.1 Condition Description ...................................................................................... 22
4.2 Efficient Precoding Matrix ............................................................................... 24
4.3 Reduced Complexity Precoding Matrix ........................................................... 29
Chapter 5 Simulation Results ...................................................................................... 34
Chapter 6 Conclusion ................................................................................................... 41
6.1 Conclusions ...................................................................................................... 41
References ...................................................................................................................... 42
Abbreviations ................................................................................................................ 48
List of Figures
Fig. 2.1. The block diagram of SC-FDE. ...................................................................... 6
Fig. 2.2. The block diagram of DDST scheme. ............................................................. 7
Fig. 2.3. The training sequence and the distortion data block are represented in time
domain and frequency domain. ...................................................................................... 10
Fig. 3.1. An ISSD scheme for DDST system. ............................................................. 14
Fig. 3.2. First hard decision for finite constellation..................................................... 16
Fig. 3.3. A process diagrams for ICS algorithm. ......................................................... 18
Fig. 4.1. Proposed structure based on DDST scheme. ................................................ 23
Fig. 4.2 (a) Cost value for the DDST system (BPSK, N=64, L=16). ............................. 32
Fig. 4.2 (b) Cost value for the rc D precoding scheme (BPSK, N=64, L=16). ........... 32
Fig. 5.1. PAPR Performance of different schemes (QPSK, N=64, L=16). ................. 37
Fig. 5.2. PAPR Performance of different schemes (QPSK, N=64, L=8). ................... 37
Fig. 5.3. Performance comparison of different schemes (BPSK, N=64, L=16). ......... 38
Fig. 5.4. Performance comparison of different schemes (BPSK, N=64, L=8). ........... 38
Fig. 5.5. Performance comparison of different schemes (QPSK, N=64, L=16). ........ 39
Fig. 5.6. Performance comparison of different schemes (QPSK, N=64, L=8). .......... 39
Fig. 5.7. Amount of searching numbers in different modulation order. ...................... 40
List of Tables
Table I Total researching numbers ......................................................................... 31
Table II Minimum distance of different schemes .................................................... 33

[1] IEEE Standard for WirelessMAN: Advanced Air Interface for Broadband Wireless
Access Systems, IEEE P802.16.1/D6, Apr. 2012.
[2] IEEE Draft Standard for Broadband over Power Line Networks: Medium Access
Control and Physical Layer Specifications, IEEE P1901/D3.00, Feb. 2010.
[3] S. H. Han and J. H. Lee, “An overview of peak-to-average power ratio reduction
techniques for multicarrier transmission transmission,” IEEE Wireless Commun.,
vol. 12, no. 2, pp. 56－65, Apr. 2005.
[4] S. Ohno and G. B. Giannakis, “Optimal training and redundant precoding for block
transmissions with application to wireless OFDM,” IEEE Trans. Commun., vol. 50,
no. 12, pp. 2113－2123, Dec. 2002.
[5] K. Hayashi and H. Sakai, “Interference cancellation schemes for single carrier
block transmission with insufficient cyclic prefix,” EURASIP J. Wireless Commun.
Netw., vol. 2008, 2008, doi:10.1155/2008/130747, Article ID 130747, 12 pp.
[6] H. Sari, G. Karam, and I. Jeanclaude, “Transmission techniques for digital
terrestrial TV broadcasting,” IEEE Commun. Mag., vol. 33, no. 2, pp. 100－109,
Feb. 1995.
[7] R. Negi and J. Cioffi, “Pilot tone selection for channel estimation in a mobile
OFDM system,” IEEE Trans. Consum. Electron., vol. 44, pp. 1122－1128, Aug1998.
[8] M. Ghogho, D. McLernon, E. Ananthram-Hernandez, and A. Swami, “Channel
estimation and symbol detection for block transmission using data-dependent
superimposed training,” IEEE Signal Process. Lett., vol. 12, no. 3, pp. 226－229,
Mar. 2005.
[9] M. Ghogho, T. Whitworth, A. Swami, and D. McLernon, “Full-rank and
rank-deficient precoding schemes for single-carrier block transmissions,” IEEE
Trans. Signal Process., vol. 57, no. 11, pp. 4433－4442, Nov. 2009.
[10] F. Wang, J. Tan, and Y. Li, “Precoded single carrier data transmission with
orthogonal frequency domain multiplexing pilots,” in Proc. IEEE ICC, Beijing,
CHN, May 2008, pp. 673－677.
[11] B. Muquet, M. d. Courville, and P. Duhamel, “Subspace-based blind and
semi-blind channel estimation or OFDM systems,” IEEE Trans. Signal Process.,
vol. 50, no. 7, pp. 1699－1712, July 2002.
[12] A. Petropulu, R. Zhang, and R. Lin, “Blind OFDM channel estimation through
simple linear precoding,” IEEE Trans. Wireless Commun., vol. 3, no. 2, pp. 647－
655, Mar. 2004.
[13] D. McLernon, E. Alameda-Hernandez, and A. G. Orozco-Lugo, “Implicitlytrained
channel estimation and equalization with zero mean input data packets,” in Proc. IEEE ISSPIT, Rome, ITA, Dec. 2004, pp. 136－139.
[14] A. G. Orozco-Lugo, M. M. Lara, and D. C. McLernon, “Channel estimation using
implicit training,” IEEE Trans. Signal Process., vol. 52, no. 1, pp. 240－254, Jan.
2004.
[15] L. Deneire, B. Gyselinckx, and M. Engels, “Training sequence versus cyclic
prefix—a new look on signal carrier communication,” IEEE Commun. Letters, vol.
5, no. 7, pp. 292－294, July 2001.
[16] S.-H. Wang, J.-C. Xie, C.-P. Li, and Y.-F. Chen, “A low-complexity PAPR
reduction scheme for OFDMA uplink systems,” IEEE Trans. Wireless Commun.,
vol. 10, no. 4, pp. 1242－1251, Apr. 2011.
[17] C. D. Chung, “Spectral precoding for constant-envelope OFDM,” IEEE Trans.
Commun. vol. 58, no. 2, pp. 555－567, Feb. 2010.
[18] M. Ma, X. Huang, B. Jiao, and Y. J. Guo, “Optimal orthogonal precoding for
power leakage suppression in DFT-based systems,” IEEE Trans. Commun. vol. 59,
no. 3, pp. 844－853, Mar. 2011.
[19] T. Hwang and Y. (G) Li “A bandwidth efficient block transmission with
frequency-domain equalization,” in Proc. IEEE 6th Circuits and Systems
Symposium on Emerging Technologies: Frontiers of Mobile and Wireless
Communication, Shanghai, China, vol. 2, June 2004, pp. 433－436.
[20] K. Takeda, H. Tomeba, and F. Adachi, “Single-carrier transmission with joint
Tomlinson-Harashima precoding and frequency-domain equalization,” in Proc.
IEEE VTS APWCS, Daejoen, KOR., Aug. 2006, pp.262－266.
[21] C.-P. Li and W.-W. Hu, “Pilot-aided ICI self-cancellation scheme for OFDM
systems,” IEEE Trans. Commun., vol. 89, no. 3, pp. 955－958, Mar. 2006.
[22] J. Lee, T. Hwang, and Y. (G) Li, “Signal detection for EST based modulation in
doubly-selective channels,” IEEE Trans. Signal Process., vol. 57, no. 48, pp. 3287
－3291, Aug. 2009.
[23] T. Hwang and Y. (G) Li, “Optimum filtering for energy-spreading transform-based
equalization,” IEEE Trans. Signal Process., vol. 55, no. 3, pp. 1182－1187, Mar.
2007.
[24] T. Hwang and Y. (G) Li, “Novel iterative equalization based on energy-spreading
transform,” IEEE Trans. Signal Process., vol. 54, no. 1, pp. 190－203, Jan. 2006.
[25] W. Wen, M. Xia, and Y.-C. Wu, “Low complexity pre-equalization algorithms for
zero-padded block transmission,” IEEE Trans. Wireless Commun., vol. 9, no. 8, pp.
2498－2504, Aug. 2010.
[26] K. Takeda, H. Tomeba, and F. Adachi, “Joint Tomlinson-Harashima precoding and
frequency-domain equalization for broadband single-carrier transmission,” IEICE
Trans. Commun., vol. E91-B, no. 1, pp. 258－266, Jan. 2008.
[27] W.-C. Huang, K.-C. Lee, C.-P. Li, and H.-J. Li, “Subcarrier power allocation in
OFDM-based dual-hop systems with AF relaying,” IEICE Trans. Commun., vol.
93, no. 11, pp. 3184－3188, Nov. 2010.
[28] J. G. Proakis, Digital Communications, 4th ed. McGraw-Hill, 2000.
[29] T. Whitworth, M. Ghogho, and D.C. McLernon, “Data identifiability for
data-dependent superimposed training,” in Proc. IEEE ICC, Glasgow, UK, June
2007, pp. 2545－2550.
[30] B. Noble and J. W. Daniel, Applied Linear Algebra. 3rd ed. Prentice Hall, 1988.
[31] D. Forney and V. Eyuboglu, “Combined equalization and coding using precoding,”
IEEE Comm. Mag., vol. 29, pp. 24－34, Dec. 1991.
[32] M. V. Clark, “Adaptive frequency-domain equalization and diversity combining
for broadband wireless communications,” IEEE J. Sel. Areas Commun., vol. 16, no.
8, pp. 1385－1395, Oct. 1998.
[33] A. V. Oppenheim, R. W. Schafer, and J. R. Buck, Discrete-Time Signal, Processing.
2nd ed., Prentice Hall, 1999.
[34] C.-P. Li and W.-C. Huang, “A constructive representation for the Fourier dual of
the Zadoff–Chu sequences,” IEEE Trans. Inf. Theory, vol. 53, no. 11, pp. 4221－
4224, Nov. 2007.
[35] K.-C- Chan, W.-C Huang, C.-P. Li, and H.-J Li, “Investigation on Data Identification Problem for Data-Dependent Superimposed Training,” in Proc.
IEEE VTC-spring, Yokohama, May. 2012, vol. 1, pp1－5.