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研究生:鄭秋君
研究生(外文):Chiu-Chun Cheng
論文名稱:改良型粒子群演算法於直序式超寬頻多用戶偵測器之應用研究
論文名稱(外文):Application of Modified Particle Swarm Optimization Algorithm to Multi-user Detection in DS-UWB System
指導教授:譚旦旭譚旦旭引用關係
口試委員:黃永發黃正光簡福榮
口試日期:2007-07-10
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:83
中文關鍵詞:直序超寬頻粒子群演算法基因演算法突變多用戶偵測器
外文關鍵詞:Direct-sequence Ultra-Wide Band (DS-UWB)Particle Swarm Optimization (PSO)Genetic Algorithm (GA)MutationMulti-user Detector (MUD)
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超寬頻無線通訊的多重路徑效應在多用戶情況下,容易影響展頻碼之間的正交特性,造成嚴重的多重存取干擾(Multi-Access Interference, MAI)。為了消除MAI,最佳多用戶偵測器雖能達到極佳的系統效能,但因計算複雜度太高,故無法實現。為了在效能與複雜度之間取得平衡,若干研究提出了次佳的方法,例如應用基因演算法之多用戶偵測器(Genetic algorithm-based multi-user detector, GA-MUD),但GA-MUD仍然存在著BER效能未盡理想及收斂速度不夠快等問題。
為改善收斂速度及提升BER之效能,我們應用粒子群演算法(Particle swarm optimization algorithm, PSO)取代GA,提出了一種新的PSO-MUD系統架構,此架構利用Rake Receiver來產生PSO初始解。另外,為了增加粒子在搜尋空間中的多元性,PSO結合了GA的突變(Mutation)機制,我們稱為PSO_Mutation-MUD。模擬結果顯示,PSO_Mutation-MUD在越惡劣通道環境中其效能改善之效果越顯著。我們比較了PSO_Mutation、PSO_CW、PSO_TCAV,以及GA等MUD之效能,模擬結果證實我們提出的PSO_Mutation-MUD架構有最好的效能。
For the multi-user application, the multiple access interference (MAI) due to imperfect orthogonality between spreading codes induced by UWB dense multi-path degrades system performance. Though optimum multi-user detector (OMD) can achieve remarkable performance, its computational complexity is too high to implement. Therefore, many sub-optimal multi-user detectors (MUDs) have been proposed to attain best trade-off between performance and complexity, such as Genetic algorithm-based multi-user detector (GA-MUD). However, BER performance of GA-MUD is not good enough and its convergence rate is slow.
In order to increase the convergence rate and BER performance, we apply Particle Swarm Optimization algorithm (PSO) to replace GA to develop a new PSO-based MUD. This MUD employs rake receiver to generate initial solution for PSO-based optimization. Moreover, we combine mutation mechanism of GA to PSO for increasing diverseness of particles in searching space which is called PSO_Mutation-MUD. Experimental results show that the proposed PSO_Mutation-MUD attains highest gain in the CM4 environment. We also compare the performances between PSO_Mutation-MUD, PSO_CW-MUD, PSO_TCAV-MUD, and GA-MUD. The result reveals that our proposed PSO_Mutation-MUD yields the best performances over UWB channels.
中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 x
第一章 緒論 1
1.1 研究動機與目的 1
1.2 研究方法 3
1.3 章節內容摘要 3
第二章 超寬頻通訊系統簡介 4
2.1 超寬頻簡介 4
2.1.1 超寬頻之定義 4
2.1.2 超寬頻特色 5
2.1.3 超寬頻應用 6
2.2 直接序列式超寬頻系統 7
2.2.1 頻帶規劃 8
2.2.2 Walsh Code 8
2.3 IEEE 802.15.3a 通道模型 9
2.3.1 無線通訊系統傳輸通道簡介 9
2.3.2 超寬頻通道模型 14
2.4 系統架構 18
第三章 多用戶偵測 23
3.1 多用戶偵測器 23
3.1.1 最佳偵測器 24
3.1.2 同步DS-UWB系統之最佳多用戶偵測器 25
3.2 耙式接收器 25
3.2.1 UWB通道模擬 27
3.2.2 模擬架構 27
3.2.3 模擬結果之探討 28
第四章 粒子群演算法 35
4.1 粒子群演算法簡介 35
4.2粒子群演算法之發展 37
4.3 粒子群演算法之演算流程 39
4.4 p-rake和PSO之效能模擬 41
4.4.1 實驗結果與討論 41
第五章 基於粒子群演算法之多用戶偵測 49
5.1 簡介 49
5.2 PSO-MUD架構 49
5.2.1 針對UWB環境之PSO-MUD架構 49
5.2.2 PSO-MUD之運算流程 50
5.3 完美功率控制條件下之模擬 57
5.3.1 PSO_Mutation-MUD及GA-MUD收斂率之比較 57
5.3.2 基於不同權重值(W)之PSO_Mutation-MUD之效能 58
5.3.2.1 分析與討論 59
5.3.3 多種MUD效能之比較 61
5.3.3.1 實驗結果與討論 61
5.3.3.2 綜合分析 71
第六章 結論與未來展望 73
參考文獻 74
縮寫中英對照 81
超寬頻規格
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[2] S. Stroh, “Ultra-Wideband: Multimedia Unplugged,” IEEE Spectrum, 2003.
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UWB通道模型
[7] C. W. Lin, Application of Genetic Algorithm to Multi-user Detection in DA-UWB System. Master Thesis, National Taipei University of Technology, June 2005.
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[20] A. Saleh and R. Valenzuela, “A Statistical Model for Indoor Multipath Propagation,” IEEE Journal on Selected Areas in Communications, pp. 128-137, Feb. 1987.

MB-OFDM
[21] A. Batra, et al., “Multi-band OFDM Physical Layer Proposal,” IEEE 802.15-03/267r6, Texas Instruments, Sep. 2003.
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DS-UWB
[23] R. Fisher, et al., “DS-UWB Proposal Update Submission to 802.15 Task Group 3a,” IEEE 802.15-04/137r3, Jul. 2004.
[24] R. Fisher, et al., “DS-UWB Physical Layer Submission to 802.15 Task Group 3a,” IEEE 802.15-04/137r3, Jul. 2004.
[25] P. Runkle, et al., “DS-CDMA : the Modulation Technology of Choice for UWB Comunications,” IEEE International Conference on Ultra Wideband Systems and Technologies, pp. 364-368, 2003.

TH-UWB
[26] E. Fishler and H. V. Poor, ”Low-Complexity Multi-user Detectors for Time-Hopping Impulse-Radio Systems,” IEEE Transaction on Signal Processing, pp. 2561-2571, 2004.
[27] M. Z. Win and R. A. Scholtz, “Ultra-wind bandwidth time-hopping spread-spectrum impulse radio for wireless multiple-access communications,’’ IEEE Transactions on Communications, vol.48, no.4, pp. 679-689, April 2000.
[28] J. Choi, “A Framework for Multiuser Detection in Time-Hopping UWB with Random Sign Repetition,” Proceedings of IEEE International Conference on Communication Systems, pp. 11–15, 2004.
[29] A. Muqaibel, B. Woerner, and S. Riad, “Application of Multiuser Detection Techniques to Impulse Radio Time Hopping Multiple Access Systems,” Proceedings of IEEE International Conference on Ultra Wideband Systems and Technologies, pp. 169 – 173, 2002.

多用戶偵測器
[30] S. Verdú, Multiuser Detection, Cambridge University Press, 1998.
[31] R. Lupas and S. Verdú, “Linear Multiuser Detectors for Synchronous Code-Division Multiple-Access Channel,“ IEEE Trans. Inform. Theory, vol. IT-35, no. 1, pp. 123-136, Jan. 1989.
[32] Z. Xie, R. T. Short, and C. K. Rushforth, “A Family of Suboptimum Detectors for Coherent Multiuser Communications,” IEEE Journal on Selected Areas in Communications, pp. 683-690, 1990.
[33] U. Madhow and M. Honig, “MMSE Interference Suppression for Direct –Sequence Spread-Spectrum CDMA,” IEEE Transaction on Communications, vol. 42, pp. 3178-3188, Dec. 1994.
[34] P. Patel and J. Holtzman, “Analysis of a Simple Successive Interference Cancellation Scheme in DS/CDMA System,” IEEE JSAC- Special Issue on CDMA, vol. 12, no. 5, pp. 796-807, June 1994.
[35] D. J. Kim, S. J. Kang, S. R. Kwon and C. E. Kang, “Multiuser Detection Using Blockwise Successive Interference Cancellation in DS/CDMA Systems,” Proceedings of VTC’98, pp. 1835-1838, May 1998.
[36] S. Haykin, Communication Systems. McMaster University Press, 2001.
[37] Q. Li and L. A. Rush, ”Multiuser Detection for DS-CDMA UWB in the Home Environment,” IEEE Transaction on Communications, pp. 1701-1711, 2002.

粒子群演算法
[38] C. Liu and Y. Xiao, “Multiuser detection using the particle swarm optimization algorithm,” 2005 IEEE International Symposium on Communications and Information Technology, vol. 1, pp. 362-365, Oct. 2005.
[39] J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, ” IEEE Transaction on Evolutionary Computation, vol. 10, no. 3, pp. 281-295, June 2006.
[40] A. Ratnaweera, S. K. Halgamuge and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Transaction on Evolutionary Computation, vol. 8, no. 3, pp. 240 – 255, June 2004.
[41] H. H. El-Mora, A. U. Sheikh and A. Sr. Zerguine, “Application of particle swarm optimization algorithm to multiuser detection in CDMA,” 2005 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 4, pp. 2522-2526, Sept. 2005.
[42] M. P. Song and G. C. Gu, “Research on particle swarm optimization: a review,” Proceedings of the IEEE International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2236-2241, Aug. 2004.
[43] R. C. Eberhart and J. Kennedy, “Particle swarm optimization,” Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948, Nov.-1 Dec. 1995.
[44] M. Dorigo, Optimization, Learning and Natural Algorithms. Ph. D. dissertation, Dipartimento di Elettronica, Politecnico di Milano, IT, 1992.
[45] J. Kennedy and R. C. Eberhart, “Swarm Intelligence,” Morgan Kaufmann Press, 2001.
[46] R. C. Eberhart and Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” 1998 Annual Conference on Evolutionary Programming, 1998.
[47] P. J. Angeline, “Using selection to improve particle swarm optimization,” IEEE World Congress on Computational Intelligence, pp. 84-89, May 1998.
[48] A. Ratnaweera, S. K. Halgamuge and H. C. Watson, “Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients,” IEEE Transactions on Evolutionary Computation, vol. 8, pp. 240-255, 2004.
[49] M. Clerc, “The swarm and the queen: towards a deterministic and adaptive particle swarm optimization,” Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1951-1957, July 1999.
[50] R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39-43, Oct. 1995.
[51] Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” Proceedings of IEEE International Conference on Evolutionary Computation, Piscataway, pp. 69-73, 1998.

基因演算法
[52] L. Davis, Handbook of Genetic Algorithm. New York: Van Norteand Reinhold, 1991.
[53] D. E. Goldberg, Genetic Algorithm in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
[54] M. J. Juntti, T. Schlosser and J. O. Lilleberg, “Genetic Algorithms for Multiuser Detection in Synchronous CDMA,” Proceedings of IEEE International Symposium on Information Theory, 1997.
[55] K. Yen and L. Hanzo, “Genetic Algorithm Assisted Joint Multiuser Symbol Detection and Fading Channel Estimation for Synchronous CDMA systems,” IEEE Journal on Selected Areas in Communications, pp. 985-998, 2001.
[56] J. H. Holland, Adaptation in Natural and Artificial systems, University of Michigan Press, 1975.

耙式接收器
[57] D. Cassioli, M. Z. Win, F. Vatalaro and A. F. Molisch, “Performance of low-complexity RAKE reception in a realistic UWB channel.” IEEE International Conference on Communications, vol. 2, pp. 763-767, May 2002.
[58] A. Rajeswaran, V. S. Somayazulu and J. R. Foerster, ” RAKE performance for a pulse based UWB system in a realistic UWB indoor channel” IEEE International Conference on Communications, vol. 4, pp. 1879-2883, May 2003.

相關連結
[59] http://www.fcc.gov/
[60] http://www.intel.com/
[61] http://www.eettaiwan.com/
[62] http://www.bluetooth.com/
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