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系統識別號 U0002-1407201410441900
中文論文名稱 應用模糊理論於感知無線網路之動態頻道配置
英文論文名稱 A Fuzzy-based Dynamic Channel Allocation Scheme in Cognitive Radio Networks
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 102
學期 2
出版年 103
研究生中文姓名 廖守立
研究生英文姓名 Shou-Li Liao
電子信箱 poiuy301@hotmail.com
學號 600410822
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2014-06-16
論文頁數 81頁
口試委員 指導教授-顏淑惠
委員-顏淑惠
委員-逄愛君
委員-李官陵
中文關鍵字 感知無線電  模糊理論  頻道配置 
英文關鍵字 Cognitive Radio  Fuzzy  Channel Allocation 
學科別分類 學科別應用科學資訊工程
中文摘要 傳統無線網路中的靜態頻譜配置方式是造成頻譜使用效率不彰的主要原因之一,為了改善這個現象,出現了一種新的網路型態,稱作感知無線網路(Cognitive Radio Network, CRN),感知無線網路採用動態頻譜存取(Dynamic Spectrum Access, DSA)技術,讓網路傳輸得以在頻譜空洞(spectrum hole)間靈活的切換使用,藉此提升頻譜使用效率。本研究考量了在感知無線網路中,由於每個次要使用者(Secondary User, SU)因為所處位置以及周遭頻譜環境的不同,會有不同的可用頻道(available channel),如何配置這些可用頻道將會是影響整個系統效能的關鍵。然而在既有機制中,並未考量到多路徑衰減(multipath fading)問題,因此,本研究提出一個改良的頻道配置機制,基於模糊理論,加入次要使用者所接收訊號強度的考量,透過模糊理論的計算,制定出次要使用者頻道存取的優先權。最終,模擬結果證明了所提機制的網路吞吐量(throughput)優於既有機制,驗證了所提機制之有效性。
英文摘要 In traditional wireless networks, fixed allocation of spectrum is one of the main reason causing low utilization of spectrum. In order to solve this problem, a new wireless communication model has been proposed, which called Cognitive Radio Networks (CRN). CRN adopts Dynamic Spectrum Access (DSA) technology, thus it can flexibly use the spectrum which primary user temporarily unused. In cognitive radio networks, due to each secondary user (SU) has different location and surrounding spectrum environment, it may have variety of available channels. How to assign these available channels is the crucial point of system performance. However, existing methods doesn’t consider the problem of multipath fading; therefore, this study proposed an improved channel allocation scheme. We consider the received signal strength to define the channel access priority of secondary users applied by fuzzy theory. Finally, the simulation results show the superior of our approach and verify the effectiveness of the proposed scheme.
論文目次 目錄
圖目錄 V
表目錄 VII
第一章 緒論 1
1-1 研究背景 2
1-2 研究動機 6
1-3 研究目的 7
1-4 論文架構 8
第二章 相關研究與背景介紹 9
2-1 感知無線網路中的頻道配置機制 10
2-1-1 具回饋的頻道配置機制 11
2-1-2 不具回饋的頻道配置機制 16
2-2 模糊理論 18
2-2-1 模糊化(Fuzzification) 20
2-2-2 模糊知識庫(Fuzzy Knowledge Base) 23
2-2-3 推論引擎(Inference Engine) 24
2-2-4 解模糊化(Defuzzification) 26
第三章 模糊動態頻道配置機制 27
3-1 網路環境設定 29
3-2 建立可用頻道表 32
3-2-1 能量偵測法(Energy Detection) 33
3-2-2 融合決策(Fusion Decision) 36
3-3 次要使用者優先權排程 38
3-3-1 次要使用者提出傳輸需求 40
3-3-2 依據歸屬函數執行模糊化 43
3-3-3 依據規則庫執行模糊推論 49
3-3-4 規則驗證及解模糊化 53
3-3-5 頻道配置 59
第四章 模擬比較與分析 60
4-1 模擬環境與參數設定 61
4-2 模擬結果與分析比較 64
第五章 結論與未來研究方向 68
5-1 結論 68
5-2 未來研究方向 69
參考文獻 70
附錄 - 英文論文 76

圖目錄
圖 1.1 Dynamic Spectrum Access (DSA) 3
圖 1.2 Cognitive Radio Network Architecture 5
圖 2.1 Cognitive cycle 10
圖 2.2 The signaling flows of Cognitive Relay 13
圖 2.3 System model for multi-band dynamic spectrum sharing 15
圖 2.4 Hierarchical system comprising two FLS 17
圖 2.5 Fuzzy Inference System 19
圖 2.6 Triangular membership function 21
圖 2.7 Trapezoidal membership function 22
圖 2.8 Linguistic Variable 23
圖 2.9 Mamdani’s min-min-max Inference 25
圖 3.1 Fuzzy-based dynamic channel allocation scheme 28
圖 3.2 System model 29
圖 3.3 Sequence diagram 30
圖 3.4 Time slots illustration 31
圖 3.5 Flow chart for establish the available channel table 32
圖 3.6 Energy Detection 33
圖 3.7 Hidden terminal problem 34
圖 3.8 Flow chart for SU priority scheduling 39
圖 3.9 Proposed Fuzzy Inference System with four input 40
圖 3.10 Membership function of Spectrum utilization Efficiency 44
圖 3.11 Membership function of Mobility 45
圖 3.12 Membership function of Distance 46
圖 3.13 Membership function of Signal Strength 47
圖 3.14 Membership function of Priority factor 48
圖 3.15 Priority factor with Signal Strength = -60dB and Distance = 350m 54
圖 3.16 Priority factor with Signal Strength = -60dB and Mobility = 15m/s 54
圖 3.17 Priority factor with Signal Strength = -60dB and Spectrum utilization Efficiency = 50% 55
圖 3.18 Priority factor with Distance=350m and Mobility = 15m/s 55
圖 3.19 Priority factor with Distance = 350m and Spectrum utilization Efficiency = 50 56
圖 3.20 Priority factor with Spectrum utilization Efficiency = 50% and Mobility = 15m/s 56
圖 3.21 Proposed Fuzzy Inference System paradigm 58
圖 4.1 Simulation environment 63
圖 4.2 Priority variation with different Signal Strength 65
圖 4.3 Case of three input (without Signal Strength) 66
圖 4.4 Analysis of average throughput per SU 66
圖 4.5 Analysis of total throughput of secondary network 67

表目錄
Table 1 Four possible scenarios for spectrum sensing 34
Table 2 Available channel table 37
Table 3 Rule Table 51
Table 4 Available channel table (update by Priority factor) 59
參考文獻 [1] FCC Spectrum Policy Task Force, "Report of the spectrum efficiency working group,” Nov. 2002.
[2] Beibei Wang and K. J. R. Liu, "Advances in cognitive radio networks: A survey," IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 1, pp. 5-23, 2011.
[3] M. T. Masonta, M. Mzyece and N. Ntlatlapa, "Spectrum decision in cognitive radio networks: A survey," IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1088-1107, 2013.
[4] Qing Zhao and B. M. Sadler, "A survey of dynamic spectrum access," IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 79-89, 2007.
[5] I. F. Akyildiz, W. Lee, M.C. Vuran and S. Mohanty, "NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey," Computer Networks, vol. 50, no. 13, pp. 2127-2159, 2006.
[6] Xiaoyuan Liu, Yanling Zhang, Yang Li, Zhongshan Zhang and Keping Long, "A survey of cognitive radio technologies and their optimization approaches," International Conference on Communications and Networking in China (CHINACOM), pp. 973-978, 2013.
[7] I. F. Akyildiz, Won-Yeol Lee, M. C. Vuran and S. Mohanty, "A survey on spectrum management in cognitive radio networks," IEEE Communications Magazine, vol. 46, no. 4, pp. 40-48, 2008.
[8] S. Haykin, "Cognitive radio: Brain-empowered wireless communications," IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, 2005.
[9] I. F. Akyildiz. (2006, June 30). Cognitive Radio Networks. Retrieved June 25, 2014, from http://www.ece.gatech.edu/research/labs/bwn/CR/
projectdescription.html
[10] R. Kaniezhil and C. Chandrasekar, "An efficient spectrum utilization via cognitive radio using fuzzy logic system for heterogeneous wireless networks," International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET), pp. 300-307, 2012.
[11] 林銀議,數位通訊原理-調變解調,台北,五南圖書,2005
[12] Chih-Wei Chen (2012). Study On Spectrum Sensing of Cognitive Radio Networks. Unpublished master’s thesis, Chi Nan University, Nantao, Taiwan
[13] Chun-Ta Kung, Ko-Wei Ma, Hung-Yu Wei, "Cognitive Relay Protocol: Design, Implementation and Evaluation", ACM SIGMOBILE Mobile Computing and Communications, vol. 14, no. 3, pp. 28-30, 2010.
[14] O. Simeone, J. Gambini, Y. Bar-Ness and U. Spagnolini, "Cooperation and Cognitive Radio," IEEE International Conference on Communications (ICC), pp. 6511-6515 , 2007.
[15] Lloyd Shapley and Martin Shubik, "The assignment game I: the core," International Journal of Game Theory, vol. 1, no. 1, pp.111-130, 1972.
[16] S. Alrabaee, M. Khasawneh, A. Agarwal, N. Goel and M. Zaman, "A game theory approach: Dynamic behaviours for spectrum management in cognitive radio network," IEEE Globecom Workshops, pp. 919-924, 2012.
[17] Dapeng Li, Youyun Xu, Jing Liu, Xinbing Wang and Zhu Han, "A Market Game for Dynamic Multi-Band Sharing in Cognitive Radio Networks," IEEE International Conference on Communications (ICC), pp. 1-5, 2010.
[18] G. I. Alptekin and A. B. Bener, "A Spectrum Trading Model with Strict Transmission Power Control," IEEE Global Telecommunications Conference (GLOBECOM), pp. 1-5, 2010.
[19] P. Kaur, M. Uddin and A. Khosla, "Fuzzy based adaptive bandwidth allocation scheme in cognitive radio networks," International Conference on ICT and Knowledge Engineering, pp. 41-45, 2010.
[20] L. A. Zadeh, "Fuzzy set", Information and Control, vol. 8, no. 3, pp.338-353, 1965.
[21] Elmer Dadios, "Fuzzy Logic– Controls, Concepts, Theories and Applications," ISBN 978-953-51-0396-7, InTech, 2012.
[22] James J. Buckley and Esfandiar Eslami, "An Introduction to Fuzzy Logic and Fuzzy Sets," ISBN 3-7908-1447-4, Physica Verlag, 2002.
[23] 萬絢、林明毅、陳宏杰,模糊理論應用與實務,台北,儒林圖書公司,2008
[24] 李允中、王小璠、蘇木春,模糊理論及其應用,台北,全華科技圖書股份有限公司,2012
[25] C. C. Lee, "Fuzzy Logic in Control Systems: Fuzzy Logic Controller - Part I and Part II," IEEE Transactions on Systems, Man and Cybernetics, vol. 20, no. 2, 1990.
[26] J. M. Mendel, "Fuzzy Logic Systems for Engineering: A Tutorial," Proceedings of the IEEE, vol.83, no.3, pp. 345-377, 1995.
[27] S. Mitaim and B. Kosko, "What is the best shape for a fuzzy set in function approximation?" IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1237-1243, 1996.
[28] S. Mitaim and B. Kosko, "The shape of fuzzy sets in adaptive function approximation," IEEE Transactions on Fuzzy Systems, vol. 9, no. 4, pp. 637-656, 2001.
[29] L. A. Zadeh, "The concept of a linguistic variable and its application to approximate reasoning I, II, III," Information Science, vol. 8, no. 3, pp. 199-249, 1975.
[30] J. A. Bernard, "Use of rule-based system for process control," IEEE Control Systems Magazine, vol. 8, no. 5, pp. 3-13, 1988.
[31] E. H. Mamdani, "Advances in the linguistic synthesis of fuzzy controllers," International Journal of Man-Machine Studies, vol. 8, no. 6, pp. 669-678, 1976.
[32] P. M. Larsen, "Industrial applications of fuzzy logic control," International Journal of Man-Machine Studies, vol. 12, no. 1, pp. 3-10, 1980.
[33] L. A. Zadeh, “Fuzzy algorithm,” Information and Control, vol. 12, no. 2, pp. 94-102, 1968.
[34] Tao Jiang and Yao Li, "Generalized defuzzification strategies and their parameter learning procedures," IEEE Transactions on Fuzzy Systems, vol. 4, no. 1, pp. 64-71, 1996.
[35] T. Yucek and H. Arslan, "A survey of spectrum sensing algorithms for cognitive radio applications," IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 116-130, 2009.
[36] K. B. Letaief and W. Zhang, “Cooperative communications for cognitive radio networks,” Proceedings of the IEEE, vol. 97, no. 5, pp. 878–893, 2009.
[37] J. Hightower, G. Borriello, and R. Want, "SpotON: An indoor 3D locationsensing technology based on RF signal strength, " University of Washington, Seattle, Univ. Washington, Tech. Rep. UW CSE 00-02-02, 2000.
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