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姓名 沈律名(Lu-Min Shen)  查詢紙本館藏   畢業系所 通訊工程學系
論文名稱 混沌粒子群優化演算法用於預失真線性化技術
(Linearization for Predistortion Using the Chaotic Particle Swarm Optimization Method)
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摘要(中) 正交分頻多工(Orthogonal Frequency Division Multiplexing,OFDM)因為高效率的頻寬效益以及多路徑通道的穩定傳輸,使得其成為現代無線通訊中不可或缺的技術。然而,此技術本身所擁有的高峰值對均值功率比(Peak-to-Average Power Ratio,PAPR)問題,造成功率放大器之非線性失真,導致調變訊號會有頻譜再生(Spectral Regrowth)的現象而干擾鄰近通道的傳輸訊號。考慮到寬頻系統中的記憶性問題,本篇論文研究了功率放大器的新預失真技術。在過去的文獻中,已有許多預失真器模型能夠同時對功率放大器進行記憶非線性化的補償。在本論文的架構中,首先將功率放大器模型的表示成一個具有記憶性的沙雷(Saleh)的模型,預失真器的部分,則是利用記憶性多項式做為預失真器的模型。與過去的預失真方法比較,提出的預失真器能透過改善混沌粒子群演算法於間接學習結構中對所提出的預失真器參數做迭代的估測,實現理想的系統性能以及加快收斂的速度,就能達成高度的線性化補償。
摘要(英) It is well known that Orthogonal Frequency Division Multiplexing (OFDM) has become indispensable in modern wireless communications because of high frequency efficiency and high transmission stability in multi-path channel environments. However, OFDM has an inherent characteristic of high Peak-to-Average Power Ratio(PAPR) subject to nonlinear distortion of a power amplifier, leading to the phenomenon of spectral regrowth for modulated signals such that the adjacent communication channels are interfered. Taking into account the memory problem in wideband systems, this thesis studied a new predistortion scheme for the power amplifier. From previous researches in the literature, there have been many predistorter’s models considering to compensate for the nonlinearity effect of a power amplifier. In our framework, we first establish the Saleh model for characterizing the power amplifier followed by a LTI model and then use the Memory polynomial model for the predistorter. Compared with previous predistortion methods, the proposed predistorter is easier to reach the required performance with a with a new chaotic particle swarm optimization(NCPSO) method at a satisfying convergence rate.
關鍵字(中) ★ 預失真
★ 粒子群演算法
★ 功率放大器
關鍵字(英) ★ predistortion
★ particle swarm optimization
★ power amplifier
論文目次 中文摘要.i
英文摘要.ii
目錄 . i
圖目錄. ii
表目錄.iii
第 1 章序論 . 1
1.1 前言 .1
1.2 章節架構 .4
第 2 章系統模型 . 5
2.1 傳輸訊號模型 .5
2.2 功率放大器模型 .6
2.2.1 記憶型 PA-Weiner Model .7
2.2.2 記憶型 PA-Hammerstein Model .8
2.2.3 非記憶型 PA-Saleh Model .8
2.4 預失真器學習結構 . 10
第 3 章補償非線性的預失真技術演算法 .12
3.1 間接學習架構自適應性算法的預失真器參數估計 .13
3.1.1 NLMS .13
3.1.2 RLS .14
3.1.3 Wiener . 15
3.1.4 KF . 16
3.1.5 UKF . 18
3.2 直接學習架構自適應性算法的預失真器參數估計 .22
3.2.2 RLS . 23
3.2.3 UKF .24
3.3 基於 PSO 算法的預失真器參數估計 .27
3.3.1 改善混沌粒子群演算法 (NCPSO) . 29
第 4 章系統模擬與結果分析 .33
4.1 間接學習架構下不同演算法比較 .34
4.1.1 星座點比較 .34
4.1.2 誤差向量幅度 .37
4.1.3 演算法收斂曲線比較 .39
4.1.4 ACPR 指標 .42
4.1.5 功率頻譜密度 .44
4.2 直接學習架構下不同演算法比較 .44
4.2.1 星座點比較 .44
4.2.2 誤差向量幅度 .46
4.2.3 演算法收斂曲線比較 . 47
4.2.4 ACPR 指標 .48
4.2.5 功率頻譜密度 .50
4.3 NCPSO 參數變化影響之比較 .51
4.4 複雜度比較 . 55
第 5 章結論 . 62
參考文獻.63
參考文獻 [1] J. Armstrong, “Ofdm for optical communications,” Journal of Lightwave Technol-
ogy, vol. 27, no. 3, pp. 189–204, Feb 2009.
[2] R. Raich, “Nonlinear system identification and analysis with applications to power
amplifier modeling and power amplifier predistortion,” Ph.D. dissertation, Geor-
gia Institute of Technology, 2004.
[3] P. B. Kenington, High Linearity RF Amplifier Design, 1st ed. Norwood, MA,
USA: Artech House, Inc., 2000.
[4] M. Gudmundson and P. O. Anderson, “Adjacent channel interference in an ofdm
system,” in Vehicular Technology Conference, 1996. Mobile Technology for the
Human Race., IEEE 46th, vol. 2, Apr 1996, pp. 918–922 vol.2.
[5] J. Pochmara, R. Mierzwiak, and K. Werner, “A combined adaptive predistortion
scheme with input back-off,” in Mixed Design of Integrated Circuits Systems,
2009. MIXDES ’09. MIXDES-16th International Conference, June 2009, pp. 583–
587.
[6] A. J. Zozaya and E. Bertran, “On the performance of cartesian feedback and feed-
forward linearization structures operating at 28 ghz,” IEEE Transactions on Broad-
casting, vol. 50, no. 4, pp. 382–389, Dec 2004.
[7] K.-P. Chan and K. K. M. Cheng, “Novel dsp algorithms for adaptive feed-forward
power amplifier design,” in Microwave Symposium Digest, 2003 IEEE MTT-S In-
ternational, vol. 2, June 2003, pp. 1323–1326 vol.2.
[8] S. Chung, J. W. Holloway, and J. L. Dawson, “Open-loop digital predistortion
using cartesian feedback for adaptive rf power amplifier linearization,” in 2007
IEEE/MTT-S International Microwave Symposium, June 2007, pp. 1449–1452.
[9] C.-H. Lin, H.-H. Chen, Y.-Y. Wang, and J.-T. Chen, “Dynamically optimum
lookup-table spacing for power amplifier predistortion linearization,” IEEE Trans-
actions on Microwave Theory and Techniques, vol. 54, no. 5, pp. 2118–2127, May
2006.
[10] J. K. Cavers, “Optimum table spacing in predistorting amplifier linearizers,” IEEE
Transactions on Vehicular Technology, vol. 48, no. 5, pp. 1699–1705, Sep 1999.
[11] L. Ding, G. T. Zhou, D. R. Morgan, Z. Ma, J. S. Kenney, J. Kim, and C. R. Giardina,
“Memory polynomial predistorter based on the indirect learning architecture,” in
Global Telecommunications Conference, 2002. GLOBECOM ’02. IEEE, vol. 1,
Nov 2002, pp. 967–971 vol.1.
[12] Z. Zeng, X. Sun, R. Lv, and Z. Yang, “Open-loop digital baseband predistortion
based on polynomials,” in Computer Science and Information Engineering, 2009
WRI World Congress on, vol. 6, March 2009, pp. 194–197.
[13] S. Choi, E. R. Jeong, and Y. H. Lee, “A direct learning structure for adaptive
polynomial-based predistortion for power amplifier linearization,” in 2007 IEEE
65th Vehicular Technology Conference - VTC2007-Spring, April 2007, pp. 1791–
1795.
[14] Z. Li, J. Kuang, and N. Wu, “Direct learning predistorter with a new loop de-
lay compensation algorithm,” in Vehicular Technology Conference (VTC Spring),
2012 IEEE 75th, May 2012, pp. 1–5.
[15] L. Ding, G. T. Zhou, D. R. Morgan, Z. Ma, J. S. Kenney, J. Kim, and C. R. Giardina,
“A robust digital baseband predistorter constructed using memory polynomials,”
IEEE Transactions on Communications, vol. 52, no. 1, pp. 159–165, Jan 2004.
[16] M. Y. Cheong, S. Werner, M. J. Bruno, J. L. Figueroa, J. E. Cousseau, and R. Wich-
man, “Adaptive piecewise linear predistorters for nonlinear power amplifiers with
memory,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 59,
no. 7, pp. 1519–1532, July 2012.
[17] L. Ding, R. Raich, and G. T. Zhou, “A hammerstein predistortion linearization de-
sign based on the indirect learning architecture,” in Acoustics, Speech, and Signal
Processing (ICASSP), 2002 IEEE International Conference on, vol. 3, May 2002,
pp. III–2689–III–2692.
[18] S. S. Haykin, Adaptive filter theory. Pearson Education India, 2008.
[19] S. Haykin and B. Widrow, Least-mean-square adaptive filters. John Wiley &
Sons, 2003, vol. 31.
[20] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Neural Networks,
1995. Proceedings., IEEE International Conference on, vol. 4, Nov 1995, pp.
1942–1948 vol.4.
[21] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in
Micro Machine and Human Science, 1995. MHS ’95., Proceedings of the Sixth
International Symposium on, Oct 1995, pp. 39–43.
[22] S. Chen, “An efficient predistorter design for compensating nonlinear memory
high power amplifiers,” IEEE Transactions on Broadcasting, vol. 57, no. 4, pp.
856–865, Dec 2011.
[23] P. L. Carro, P. G. Ducar, J. de Mingo, and A. Valdovinos, “Nonlinear distortion
cancellation using particle swarm optimization (pso) based predistortion in ofdm
systems,” in 2007 16th IST Mobile and Wireless Communications Summit, July
2007, pp. 1–5.
[24] A. H. Abdelhafiz, O. Hammi, A. Zerguine, A. T. Al-Awami, and F. M. Ghan-
nouchi, “A pso based memory polynomial predistorter with embedded dimension
estimation,” IEEE Transactions on Broadcasting, vol. 59, no. 4, pp. 665–673, Dec
2013.
[25] P. L. Carro, P. G. Ducar, J. de Mingo, and A. Valdovinos, “Out-of-band distortion
reduction in dvb-t systems with particle swarm digital predistorters,” in 2007 IEEE
66th Vehicular Technology Conference, Sept 2007, pp. 1312–1316.
[26] H. Liu, “Efficient mapping of range classifier into ternary-cam,” in Proceedings
10th Symposium on High Performance Interconnects, 2002, pp. 95–100.
[27] C. Tellambura, “Computation of the continuous-time par of an ofdm signal with
bpsk subcarriers,” IEEE Communications Letters, vol. 5, no. 5, pp. 185–187, May
2001.
[28] Y. Ding, H. Ohmori, and A. Sano, “Adaptive predistortion for high power amplifier
with linear dynamics,” in Circuits and Systems, 2004. MWSCAS ’04. The 2004 47th
Midwest Symposium on, vol. 3, July 2004, pp. iii–121–4 vol.3.
[29] C. Rapp, “Effects of HPA-nonlinearity on a 4-DPSK/OFDM-signal for a digital
sound broadcasting signal,” in ESA Special Publication, ser. ESA Special Publi-
cation, P. S. Weltevreden, Ed., vol. 332, Oct. 1991.
[30] A. Y. Kibangou and G. Favier, “Wiener-hammerstein systems modeling using
diagonal volterra kernels coefficients,” IEEE Signal Processing Letters, vol. 13,
no. 6, pp. 381–384, June 2006.
[31] P. Gilabert, G. Montoro, and E. Bertran, “On the wiener and hammerstein models
for power amplifier predistortion,” in 2005 Asia-Pacific Microwave Conference
Proceedings, vol. 2, Dec 2005, pp. 4 pp.–.
[32] D. R. Morgan, Z. Ma, J. Kim, M. G. Zierdt, and J. Pastalan, “A generalized
memory polynomial model for digital predistortion of rf power amplifiers,” IEEE
Transactions on Signal Processing, vol. 54, no. 10, pp. 3852–3860, Oct 2006.
[33] B. Feuvrie, M. Diop, and Y. Wang, “Efficient baseband digital predistorter using
lut for power amplifier (pa) with memory effect,” American Journal of Electrical
and Electronic Engineering, vol. 2, no. 3, pp. 72–81, 2014. [Online]. Available:
http://pubs.sciepub.com/ajeee/2/3/3
[34] S. S. Haykin, Kalman Filtering and Neural Networks. New York, NY, USA: John
Wiley & Sons, Inc., 2001.
[35] X. Zhang and Y. Cao, “A novel chaotic map and an improved chaos-based image
encryption scheme,” The Scientific World Journal, vol. 2014, p. 8, 2014. [Online].
Available: http://dx.doi.org/10.1155/2014/713541
[36] C. Eun and E. J. Powers, “A predistorter design for a memory-less nonlinearity
preceded by a dynamic linear system,” in Global Telecommunications Conference,
1995. GLOBECOM ’95., IEEE, vol. 1, Nov 1995, pp. 152–156 vol.1.
指導教授 張大中(Da-Chung Chang) 審核日期 2018-8-16
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