基因演算法於802.11AC AP 佈建干擾問題之應用

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：52

、訪客IP：3.145.91.111

姓名

徐羽模(Yu-Mo Hsu) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基因演算法於802.11AC AP 佈建干擾問題之應用
(Application of Genetic Algorithms in the deployment and interference problem of 802.11AC AP)

相關論文

★ 利用手持式手機工具優化行動網路系統於特殊型活動環境	★ 穿戴裝置動態軌跡曲線演算法設計
★ 石英諧振器之電極面設計對振盪頻率擾動之溫度相依性研究	★ 股票開盤價漲跌預測
★ 感知無線電異質網路下以不完美頻譜偵測進行資源配置之探討	★ 大數量且有限天線之多輸入多輸出系統效能分析
★ 具有元學習分類權重轉移網路生成遮罩於少樣本圖像分割技術	★ 具有注意力機制之隱式表示於影像重建三維人體模型
★ 使用對抗式圖形神經網路之物件偵測張榮	★ 基於弱監督式學習可變形模型之三維人臉重建
★ 以非監督式表徵分離學習之邊緣運算裝置低延遲樂曲中人聲轉換架構	★ 基於序列至序列模型之 FMCW雷達估計人體姿勢
★ 基於多層次注意力機制之單目相機語意場景補全技術	★ 應用於3GPP WCDMA-FDD上傳鏈路系統的遞迴最小平方波束合成犛耙式接收機
★ 調適性遠時程瑞雷衰退通道預測演算法設計與性能比較	★ 智慧型天線之複合式到達方位-時間延遲估測演算法及Geo-location應用

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

隨著無線通訊的進步，IEEE 802.11 也不例外，近兩年IEEE(電機電子工程
師協會)制訂了新的規格IEEE 802.11AC，之前IEEE 802.11n 常用的2.4GHz(ISM
Band)已趨近於飽和，IEEE 802.11AC 則選擇工作在5GHz，相較之下訊號受到干
擾的情況也大幅降低。現在802.11 無線基地台已成為多數人不可或缺的生活工
具，主要原因有三點，1、成本不高，大多數人都可負擔。2、架設簡單，不需要
專人幫忙架設。3、傳輸速度快，足夠供給區域網路(LAN)內的使用者使用。綜合
以上優點，多數單位機關都會大量的佈建802.11 無線基地台，以提供區域內使
用者能夠有方便的無線網路可使用。然而，多數人佈建時並不會考量到同頻道或
鄰近頻道互相干擾的問題，導致覆蓋邊緣地帶會被自己或其他單位架設無線基地
台所干擾，因而導致訊號不穩或是速度過慢的情況發生。在本篇論文，採用基因
演算法來解決大面積覆蓋時所產生的問題，除了顧及各個無線基地台的SINR（訊
號干擾加雜訊比）所造成的干擾產生的覆蓋率不同，並錯開各個鄰近無線基地台
頻道，再進行Matlab 的模擬。

摘要(英)

As the progress of wireless communications, IEEE 802.11 is no exception. IEEE
（Institute of Electrical and Electronics Engineers）formulates new specification
IEEE802.11AC in the past two years. 2.4GHz（ISM） of IEEE802.11n is almost
saturated，and 802.11AC operates at 5GHz. In contrast, the signal has a smaller
chance having interference. Nowadays, 802.11 AP (Access Point) is one of our
necessary tools. It has three reasons：1.The cost is not expensive and everyone could
afford the AP router. 2. Setting up is not complicated. 3. The throughput is good and
the AP router can supply WLAN to users. Based on the above advantages，most
organizations could deploy lots of AP router for local users. Everyone usually ignores
channel interference problems. Sometimes, AP routers are interfered by other
company’s AP or set up AP by their own selves, which causes weak WLAN signal or
bad throughput. In this thesis，we use the genetic algorithm for solving the coverage
problem in the deployment of AP routers. We calculate SINRs among APs for the
radiation of AP routers. The AP router‘s channels are staggered. The simulation is
conducted to verify the efficacy of the proposed method.

關鍵字(中)

★ IEEE802.11AC
★ 覆蓋率
★ 基地台佈建

關鍵字(英)

★ IEEE 802.11AC
★ Coverage
★ WLAN AP deployment

論文目次

目錄
摘要................................................................ I
ABSTRACT........................................................... II
誌謝.............................................................. III
表目錄............................................................. VI
圖目錄............................................................ VII
第一章緒論........................................................ 1
1.1 研究動機.................................................... 1
1.2 研究目的.................................................... 1
1.3 研究內容及方法.............................................. 2
1.4 各章提要.................................................... 2
第二章 IEEE 802.11AC 簡介 .......................................... 3
2.1 何謂IEEE 802.11AC .......................................... 3
2.2 工作頻段.................................................... 4
2.3 頻寬........................................................ 5
2.4 調變/碼率................................................... 6
2.5 MIMO Spatial Streams & Multi-user MIMO...................... 6
2.6 Beam forming................................................ 7
2.7 Data rate................................................... 7
第三章基因演算法應用.............................................. 9
3.1 基因演算法簡介.............................................. 9
3.2 基因演算法組成的四個元素................................... 11
3.3 802.11AC 通道環境研究...................................... 17
3.4 SINR(Signal to Interference-plus-Noise ratio).............. 29
第四章模擬結果與分析............................................. 31
4.1 模擬工具................................................... 31
4.2 研究方法................................................... 31
V
4.3 建立模擬環境............................................... 31
4.4 世代進化................................................... 35
4.5 效能分析與比較............................................. 35
第五章結論....................................................... 43
第六章參考文獻................................................... 44

參考文獻

[1] IEEE 802.11AC: What Does it Mean for Test? - LitePoint：
http://www.litepoint.com/whitepaper/80211AC_Whitepaper.pdf
[2] Mirin Lew, Agilent Technologies〝Introduction to 802.11AC WLAN Technology
and Testing〞:
http://www.home.agilent.com/agilent/home.jspx?lc=cht&cc=TW
[3] Data rates：http://en.wikipedia.org/wiki/IEEE_802.11AC
[4] 台灣區電機電子工業同業工會電子報：
http://www.teema.org.tw/epaper/20090218/industrial004.html
[5] 林昇甫、徐永吉，遺傳演算法及其應用，一版，五南書局，民國98年
[6] 林美杏，〝車載通訊系統研究與IEEE 802.11p效能分析〞，國立中央大學，
碩士論文,民國100年
[7] T. S. Rappaport, 〝Wireless communications: principles and practice 2nd edition,〞
Pearson Education International, 2002.
[8] S.A.C Schuckers,N.A. Schmid,A Abhyankar,V Dorairaj,C K Boyce, and L. A.
Hornak,〝On techniques for angle compensation in nonideal iris recongnition,〞
IEEE Trans.Syst,Man,Cybern.,Part B,vol.37,no 5,pp.1176-1190,2007
[9] Z.Sun,Y. Wang,T. Tan, and J. Cui,〝Improving iris recognition accuracy via
cascaded classifiers,〞IEEE Trans. Syst.Man,Cybern,Part C,vol.35,no.3pp
435-441,2005
[10] P.Broussard,R. Ives, and W. Robert, 〝Using artificial neural networks and
feature saliency to identify iris measurements that contain the most discriminatory
information for iris segmentation,〞in Proc.IEEE Int.Conf.Computational
Intelligence in Biometrics:Theory,Algorithms,and Application,pp.46-51,2009.
[11] M. Vatsa,R. Singh, A. Noore ,〝Improving iris recognition performance using
45
segmentation, quality enhancement match score fusion ,and indexing,〞IEEE
Trans Syst.,Man,Cybern,Part B,vol.38 no.4,pp.1021-1035,2008.
[12] L. Ma, T. Tan, Y. Wang, and D. Zhang, 〝Personal identification based on iris
texture analysis,〞IEEE Trans. Pattern Analysis and Machine Intelligence,vol.25,
no.12,pp.1519-1533,2003.
[13] X. Chang and J. H. Lilly,〝Evolutionary design of a fuzzy classifier from data,〞
IEEE Trans. Syst.,Man, Cybern.,Part B ,vol.31,pp.426-432,2001.
[14] H. M. Lee,C. M. Chen, J. M. Chen, and Y. L. Jou,〝An efficient fuzzy classifier,〞
IEEE Trans. Syst.,Man Cybern.,Part B,vol.31,no. 3,pp.426-432,2001.
[15] P. Meesad and G. G. Yen,〝Conbined numerical and linguistic knowledge
representation and its application to medical diagnosis〞IEEE Trans. Syst., Man,
Cybern., Part B, vol.33,no2,pp.206-222,2003.
[16] A. Chatterjee and A. Rakshit〝, Influential rule search schem（e IRSS）-a new fuzzy
pattern classifier,〞IEEE Trans. Knowledge and Data Engineering,vol. 16, no.
8,pp.881-893,2004.
[17] N. S. Chaudhari,A. Tiwari, and J. Thomas,〝Performance evalution of SVM
based semi-supervised classification algorithm,〞in Proc. IEEE Int. Conf.
Conreol, Automation.Robotics and Vision,pp.1942-1947,2008.
[18] H. Ishibuchi and T. Nakaskima,〝Improving the performance of fuzzy classifier
systems for pattern classication problems with continuos attributes,〞IEEE Trans.
Industrial Electronics, vol.46, no. 6, pp. 1057-1068,1999.
[19] M. S. Kim,C. H. Kim, and J. J. Lee,〝classifying neuro-biological signals by
evolutionary fuzzy classifier construction.〞in Proc. IEEE Int. Conf. Annual , vol.
2, pp.1813-1818,2004.
[20] S. Abe,〝Dynamic cluster generation for a fuzzy classifier with ellipsoidal
regions, 〞IEEE Trans. Syst., Man, Cybern., Part B, vol.28, no. 6, pp.
46
869-879,1998.
[21] T. Yang and L. Yao,〝A fuzzy classigier with adaptive learning of norm inducing
matrix,〞in Proc. IEEE Int. Conf. Networking, Sensing and Control, pp.
362-367,2007.
[22] S. Abe, R. Thawonmas, and M. Kayama, 〝A fuzzy classifier with ellipsoidal
regions for diagnosis problems,〞IEEE Trans. Syst., Man, Cybern., Part C, vol.
29, no. 1,pp. 140,1999.
[23] A. Bankar and M. F. Azeem,〝Input selection for TSK fuzzy model based on
modified mountain clustering,〞in Pro. IEEE Int. Conf. Intellgent System, pp.
295-299,2006.
[24] M. Cococcioni, B. Lazzerini,and F. Marcelloni,〝A TSK fuzzy model for
combining outputs of multiple classifiers,〞in Proc. IEEE Int. Conf. Fuzzy.
Information, vol. 2, pp.871-876,2004.
[25] K. Kim; Y. Kim, E. Kim, and M. Park,〝New TSK fuzzy modeling approach,〞in
Proc. IEEE Int. Conf. Fuzzy System, vol.2, pp. 773-776,2004.
[26] A. Kumar. D. P. Agrawal, and S. D. Joshi,〝A GA-based method for constructing
TSK fuzzy rules from numerical data,〞in Proc. IEEE Int. Conf. Fuzzy System,
vol.1, pp. 131-136,2003.
[27] T. Hatanaka, Y. Kawaguchi, and K. Uosaki,〝Nonlinear system identification
based on evolutionary fuzzy modeling,〞Proc. IEEE Int. Conf. Evolutionary
Computation, Vol. 1,pp. 646-651,2004.
[28] S. Qu, X. Wang, and M. Gong,〝Synchronization of unified chaotic systems and
application to secure communication,〞in Proc. IEEE Int. Conf. Control,
pp.370-373,2008.
[29] S. C. Qu, X. Y. wang, and M. J. Gong,〝Secure communication based on
synchronization of unified chaotic systems,〞in Proc. IEEE Int. Conf. Itelligent
47
Information Hiding and Multimedia Signal Processing,pp.1336-1339,2008.
[30] T. Yang and L. O. chua,〝Impulsive Stabilization for control and synchronization
of chaotic systems : theory and application to secure communication,〞IEEE
Trans. Circuits and Systems I : Fundamental Theory and applications, vol. 44,
no.10 , pp.976-988,1997.
[31] D. M. Li,〝Identification of chaotic systems with large noise based on regularized
feedforward neural networks,〞in Proc. IEEE Int. Conf. Machine Learning and
Cybernetics, vol. 7, pp.4060-4063,2005.
[32] G. P. Jiang, G. Chen, and W. K. S. Tang,〝Stabilizing unstable equilibria of
chaotic systems from a State observer approach,〞IEEE Trans. Circuits and
Systems II: Express Briefs, vol.51, no.6, pp.281-288,2004.
[33] G. Qian, X. Zhou, and S. Qiu,〝Chaotic control of nonlinear systems based on
improving cross correlation,〞in Proc. IEEE Int. Conf. Communications, Circuits
and Systems Proceedings,vol.4,pp.2381-2384,2006.
[34] H. B. Xu, B. C. Lu, and G. Chen〝, Chaotic control of a nonlinear continuous-time
system with uncertainty,〞in Proc. IEEE Int. Conf. Intelligent Control and
Automation,vol.5,pp.3274-3276.2000.
[35] Matlab history: http://zh.wikipedia.org/zh-tw/MATLAB

指導教授

陳永芳(Yung-Fang Chen)

審核日期

2013-7-15

推文