臺灣博碩士論文加值系統

在直接序列分碼多重進接系統（Direct Sequence Code Division Multiple
Access，DS-CDMA）下處理多重進接干擾（Multiple Access Interference，MAI）
的問題，通常利用基因演算法（Genetic Algorithm，GA）、模擬退火演算法
（Simulated Annealing，SA）等啟發式（Heuristic）演算法來達到最佳化多使用
者偵測（Optimum Multiuser Detection，OMUD）。在本論文中，我們以粒子群
優演算法（Particle Swarm Optimization，PSO）作為解決最佳化多使用者偵測的
方法。PSO 具有快速收斂、運算複雜度較低、對最佳解的搜尋有很好表現等優點。
為了提升PSO 的效能以及減少參數，我們設計幾種慣性權重控制粒子群優演算
法（inertia Weighting controlled Particle Swarm Optimization，W-PSO）以及參數
簡化粒子群優演算法（Reduced-parameter Particle Swarm Optimization，R-PSO）
做模擬與比較。與現有演算法比較起來，由模擬結果證實所提出的新型PSO 演
算法可以更快逼近最佳偵測器的效能，並且在多使用者偵測上有較低的運算複雜
度以及更快的收斂速度。

In direct-sequence code division multiple access (DS-CDMA) systems, the
heuristic optimization algorithms for multiuser detection include genetic algorithms
(GA) and simulated annealing (SA) algorithm. In this thesis, we use particle swarm
optimization (PSO) algorithms to solve the optimization problem of multiuser
detection (MUD). PSO algorithm has several advantages, such as fast convergence,
low computational complexity, and good performance in searching optimum solution.
In order to enhance the performance and reduce the number of parameters, we
propose two modified PSO algorithms, inertia weighting controlled PSO (W-PSO)
and reduced-parameter PSO (R-PSO). From simulation results, the performance of
our proposed algorithms can achieve that of optimal solution. Furthermore, our
proposed algorithms have faster convergence performance and lower complexity
when compared with other conventional algorithms.

誌謝................................................................................................................................ i
摘要............................................................................................................................... ii
Abstract ........................................................................................................................ iii
目錄............................................................................................................................... iv
圖目錄........................................................................................................................... vi
表目錄........................................................................................................................ viii
第一章簡介.................................................................................................................. 1
1.1 研究背景............................................................................................................ 1
1.2 研究動機............................................................................................................ 4
1.3 論文架構............................................................................................................ 5
第二章 DS-CDMA 系統多使用者偵測 ..................................................................... 5
2.1 直接序列分碼多重進接系統簡介................................................................... 5
2.2 DS-CDMA 之多使用者偵測 ............................................................................ 6
2.2.1 同步DS-CDMA 系統下的基頻帶訊號模型 ...................................... 7
2.2.2 傳統匹配濾波偵測器........................................................................... 8
2.2.3 解相關偵測器..................................................................................... 10
2.2.4 最小均方誤差偵測器......................................................................... 11
2.2.5 干擾消除多用戶偵測器..................................................................... 13
2.2.6 最佳化偵測器..................................................................................... 14
第三章演化式演算法之多使用者偵測.................................................................... 15
3.1 啟發式演算法基本概念.................................................................................. 15
3.2 基因演算法...................................................................................................... 15
3.3 模擬退火演算法.............................................................................................. 21
3.4 粒子群優演算法.............................................................................................. 24
3.5 啟發式演算法比較......................................................................................... 28
3.5.1 比較三種演算法的相同處................................................................. 28
3.5.2 比較三種演算法的不同處................................................................. 29
第四章改良式粒子群優演算法之設計.................................................................... 30
4.1 慣性權重控制粒子群優演算法..................................................................... 31
4.1.1 線性慣性權重模型............................................................................. 32
4.1.2 對數慣性權重模型............................................................................. 33
4.1.3 指數慣性權重模型............................................................................. 34
4.2 簡化粒子群優演算法..................................................................................... 35
第五章模擬與比較.................................................................................................... 36
5.1 直接序列分碼多重進接系統模擬.................................................................. 36
5.1.1 產生直接序列分碼多重進接系統..................................................... 36
5.1.2 直接序列分碼多重進接系統模擬情形............................................. 37
5.2 改良式粒子群優演算法用於最佳化問題的模擬與比較............................. 41
5.2.1 常數慣性權重模型之模擬................................................................. 41
5.2.2 線性慣性權重模型之模擬................................................................. 44
5.2.3 對數慣性權重模型之模擬................................................................. 45
5.2.4 指數慣性權重模型之模擬................................................................. 46
5.2.5 負指數慣性權重模型之模擬............................................................. 49
5.2.6 其他慣性權重粒子群優演算法之設計與模擬................................. 52
5.2.7 簡化型粒子群優演算法參數設計之模擬......................................... 55
第六章結論與未來展望............................................................................................ 58
附錄A .......................................................................................................................... 60
參考文獻...................................................................................................................... 63

[1] R. L. Pickholtz, L. B. Milstein, D. L. Schilling, “Spread spectrum for mobile
communications,” IEEE Trans. Veh. Technol., vol. 40 Issue 2, pp. 313 -322, May
1991.
[2] S. Haykin, Communication Systems, 4th Ed., Wiley, 2000.
[3] S. Verdu, Multiuser Detection, Cambridge, U.K.: Cambridge Univ. Press, 1998.
[4] M. Jiang and L. Hanzo, “Multiuser MIMO-OFDM for Next-Generation Wireless
Systems,” Proc. IEEE, pp. 1430-1469, Jul. 2007.
[5] S. Verdu, “Minimum probability of error for asynchronous Gaussian multiple
access channels,” IEEE Trans. Info. Theory, vol. 32, no. 1, pp. 85-96, Jan. 1986.
[6] J. Kennedy, and R. C. Eberhart, “Particle swarm optimization,” Proc. IEEE
International Conf. Center, pp. 1942-1948, Piscataway, NJ, 1995.
[7] L. Hongwu and L. Ji, “A particle swarm optimization-based multiuser detection
for receiver-diversity aided STBC systems,” IEEE Signal Processing Letter, vol.
15, pp. 29-32, 2008.
[8] W. Yao, S. Chen, S. Tan, and L. Hanzo, “Particle swarm optimization aided
minimum bit error rate multiuser transmission,” IEEE ICC 2009, pp. 1-5, 2009.
[9] Z. Guo, Y. Xiao and M. H. Lee, “Multiuser detection based on particle swarm
optimization algorithm over multipath fading channels,” IEICE Trans. Commun.,
vol. E90-B, pp. 421-424, 2007.
[10] R. Lupas and S. Verdu, “Linear multiuser detector for synchronous code division
multiple-access channels,” IEEE Trans. Inform. Theory, vol. 35, no. 1, pp.
123-136, Jan. 1989.
[11] B. Manatsavee, K. Ahmed, and A. Fernando, “Performance of PIC, SIC and
decorrelating detectors for MUD technique in WCDMA system,” in Proc.
ICICS-PCM, vol. 2, pp. 892-896, Dec. 2003.
[12] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated
annealing,” Science, vol. 220, pp. 671-680, May 1983.
[13] X. F. Wang, W. S. Lu, and A. Antoniou, “A genetic-algorithm-based multiuser
detector for multiple-access communications,” in Proc. ISCAS 1998, vol. 4, pp.
534-537, Monterey, CA, May 31-June 5, 1998.
[14] T. Abrao, F. Ciriaco, L. D. Oliveira, B. A. Angelico, P. Jeszensky, and F.
Casadevall, “Weighting particle swarm, simulation annealing and local search
optimization for S/MIMO MC-CDMA systems,” in IEEE Swarm Intelligence
Symposium, pp. 1-7, Sept 21-23, 2008.
[15] T. Abrao, F. Ciriaco, L. D. Oliveira, B. A. Angelico, P. Jeszensky, and F.
Casadevall, “GA, SA, and TS near-optimum multiuser detectors for s/MIMO
MC-CDMA systems,” Digital Object Identifier 10.1109/WCSN, pp. 173-178,
2008.
[16] D. Whitley, “A genetic algorithm tutorial,” Statistics and Computing, vol. 4, pp.
65-85, 1994.
[17] M. Jiang, S. X. Ng, and L. Hanzo, “Hybrid iterative multiuser detection for
channel coded space division multiple access OFDM systems,” IEEE Trans. Veh.
Technol., vol. 55, no. 1, pp. 115-127, Jan. 2006.
[18] S. Chen, W. Yao, H. R. Palally, and L. Hanzo, “Particle swarm optimization
aided MIMO transceiver designs,” Computational Intelligence in Expensive
Optimization Problems, pp. 487-511, Springer-Verlag, 2010.
[19] Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” in Proc. IEEE
International Conference on Evolutionary Computation, Alaska, May 1998.
[20]K. K. Soo, Y. M. Siu, W. S. Chan, L. Yang, and R. S. Chen,
“Particle-swarm-optimization based multiuser detector for CDMA
communications,” IEEE Trans. Veh. Technol., vol. 56, Issue 5, part 2, pp.
3006-3013, 2007.
[21] Y. Zhao, and J. Zheng, “Particle swarm optimization algorithm in signal
detection and blind extraction,” in 7th International Symposium on Parallel
Architectures, Algorithms and Networks, pp. 37-41, May 2004.
[22] Y. Zhao and J. L. Zheng, “Multiuser detection using the particle swarm
optimization algorithm in DS-CDMA communication systems,” Tsinghua Univ
(Sci & Tech), vol. 44, pp. 840-842, June, 2004.
[23] L. Zhen-su, Y. Shi, “Multiuser Detector Based on Particle Swarm Algorithm,”
IEEE 6th Circuits and Systems Symposium on Emerging Technologies: Frontiers
of Mobile and Wireless Communication, vol. 2, pp. 783-786, May 31-June 2,
2004.
[24] J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm
algorithm,” Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics, Vol. 5, pp.
4104-4108, 1997.