跳到主要內容

臺灣博碩士論文加值系統

(44.192.247.184) 您好!臺灣時間:2023/02/06 11:35
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:吳振鋒
研究生(外文):Chen-Feng Wu
論文名稱:無線行動網路中之預測式通話許可控制方法
論文名稱(外文):Prediction-Based Call Admission Control Schemes for Wireless Cellular Networks
指導教授:李良德李良德引用關係
指導教授(外文):Liang-Teh Lee
學位類別:博士
校院名稱:大同大學
系所名稱:資訊工程學系(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:英文
論文頁數:135
中文關鍵詞:通話許可控制服務品質通話丟棄率通話阻塞率隱藏式馬可夫模型
外文關鍵詞:CAC (Call Admission Control)QoS (Quality of Service)CDP (Call Dropping Probability)CBP (Call Blocking Probability)HMM (Hidden Markov Models)SMPL (SiMulation Programming Language)
相關次數:
  • 被引用被引用:0
  • 點閱點閱:183
  • 評分評分:
  • 下載下載:35
  • 收藏至我的研究室書目清單書目收藏:4
由於無線技術的快速發展,蜂巢式網路也由第一代演變至第三代甚至越過第三代。因為較新的蜂巢技術可以提供較高速度的傳輸,所以提供給顧客的服務也逐漸由語音轉變為多媒體。因此多媒體服務將逐漸成為下一代網路的主流,並且由於在蜂巢網路中的無線頻寬有限,通話許可控制(CAC)將扮演確保維護服務品質(QoS)的重要角色。因為使用者移動性是相當的不確定,所以要同時達到維持良好的通話丟棄率(CDP)與通話阻塞率(CBP)的目標是較困難的,並且很少研究同時針對這些議題。因此,配合通話許可控制方案加入適當的預測資訊,將有助於維持良好的通話丟棄率與通話阻塞率。
已有許多的研究專注在使用移動性探討,其不外以下兩種考量:1)蜂巢方法主要在記錄每一時間每一蜂巢的轉接統計;2)使用者方法主要在計算每一使用者漫遊概況與記錄在通話期間所拜訪的蜂巢。同樣地在通話許可控制方案的設計問題上,也應該有不同的場景考量。此外對於蜂巢網路操作而言,將根據實際情況需求所產生的使用者移動性預測與通話許可控制方案做整合,將會是一重要議題。
在使用者移動性預測上,本論文中依據可能因地理限制、個別資訊的獲取等實際場景因素,同時提出兩種不同的使用者移動性預測方法,以建立兩種結合不同預測方法的通話許可控制方案。在使用者方法方面,採用適合動態局勢的隱藏式馬可夫模型觀念於預測使用者移動性,接著提供資訊給對應提出的通話許可控制方案;就蜂巢法方而言,則採用可以預測如系統使用率與通話下降率等大部分資訊的時間序列預測方法,與對應提出的通話許可方案作結合。
在本論文中採用以“SMPL”所撰寫的模擬程式來評估所提出方法的效能。使用者與蜂巢兩種方法的模擬結果,在通話下降與通話阻塞方面,都顯示出所提出的通話許可方案優於傳統的頻道守衛方案,並且所提出的方案亦可達到較好的系統使用效率。因此,根據模擬結果,結合適當使用者移動預測方法所提出的通話許可控制方案的確可以對無線蜂巢網路做出貢獻。
Due to the rapid progression for wireless technology, the cellular networks are also evolved from the first generation to the third generation and even beyond the third generation further. The subscriber services are gradually changed from voice to multimedia since higher speed of transmission will be provided by newer cellular technologies. Consequently, the multimedia services are getting to become the major trend in next-generation cellular networks, and the Call admission control (CAC) will play the key role for guaranteeing the quality of service (QoS) in wireless cellular networks, owing to the scarce wireless spectrum. Because the user mobility is so indeterminate, the goal which keeps both call dropping probability (CDP) and call blocking probability (CBP) below a lower level simultaneously is more difficult and seldom literatures were focused on such issue. Thus, the appropriate prediction information will be useful for a CAC scheme to meet QoS issues of both CDP and CBP at the same time.
There have been so many studies focusing on the prediction of user mobility, and they are fallen into two considerations: the cell approach consists in recording handoff statistics on each cell each time; the user approach consists in computing a roaming profile for each user and recording each visited cell during a call. The design issues of a CAC should be also considered in different scenarios by the same token. Moreover, it will be an important issue for the operation of cellular networks to integrate CAC scheme with the prediction of user mobility produced according to the demand of real situation.
In terms of prediction of user mobility, two CAC schemes that are coupled with different prediction methods are proposed in this dissertation. Upon actually using scenarios that may be geography constraint, possession constraint of individual information, and so on, two prediction methods for user mobility are proposed simultaneously. For user approach, the Hidden Markov Models (HMM) concept which is suitable for solving a dynamic situation is applied to predict user mobility then provides information for the correspondingly proposed CAC scheme, and the time series prediction which can predict most information, such as system utilization and CDP, is combined with the correspondingly proposed CAC scheme for cell approach.
The SMPL (SiMulation Programming Language) has been adopted for the simulation to evaluate the performance of the proposed schemes. The simulation results of both user and cell approaches show that the proposed CAC schemes are superior to the traditional guard channel schemes in terms of CDP and CBP, and better utilization can be also achieved by the proposed schemes. Indeed, according to the simulation results, the proposed CAC schemes that are combined with the appropriate prediction method of user mobility can do some contributions on wireless cellular networks.
ACKNOWLEDGMENTS I
ABSTRACT II
CHINESE ABSTRACT IV
TABLE OF CONTENTS VI
LIST OF FIGURES IX
LIST OF TABLES XII
CHAPTER 1 INTRODUCTION 1
1.1 Revolution of Wireless Communications 1
1.2 Current Mobile Radio Communication around the World 3
1.3 Demands on Seamless Access 7
1.4 Motivation 8
1.5 Organization 10
CHAPTER 2 Background 11
2.1 Concepts of Cell 11
2.2 Handoff Methodology 15
2.2.1 Handoff Process 15
2.2.2 Prioritized Handoffs 16
2.3 Channel Assignment Strategies 17
2.3.1 Fixed Channel Assignment Strategies 18
2.3.2 Dynamical Channel Assignment Strategies 19
2.3.3 Flexible Channel Assignment Strategies 20
2.4 User Mobility 21
2.4.1 Analysis of Mobility Model 22
2.4.2 Mobility Prediction 23
2.5 Call Admission Control 25
2.5.1 Classifications of CAC 25
2.5.2 Traditional CAC approaches 27
CHAPTER 3 THE PROPOSED USER-BASED CAC SCHEME 32
3.1 Related Works 32
3.2 The HMMs 33
3.2.1 Fundamental of HMMs 34
3.2.2 The Solutions of HMM Problems 36
3.3 Design Considerations 38
3.4 The Prediction of User Mobility 40
3.5 The HPTCAC Algorithm 44
CHAPTER 4 THE PROPOSED CELL-BASED CAC SCHEME 51
4.1 Related Works 51
4.2 Overview of Time Series Prediction 53
4.3 The Agent Concept in the Proposed Scheme 54
4.4 The Design Methodology of the Proposed Prediction Scheme 55
4.4.1 Sub-module for Time Series Data Analysis 57
4.4.2 Sub-module for Time Series Prediction 58
4.4.3 Sub-module for Compensation Prediction 60
4.4.4 Sub-module for Prediction Selection 61
4.4.5 Design Considerations 62
4.5 The Proposed CAC Scheme 63
CHAPTER 5 PERFORMANCE ANALYSIS 68
5.1 Simulation and Modeling 68
5.1.1 Discrete-event Simulation 69
5.1.2 Simulation of Wireless Network in SMPL 71
5.2 Performance Evaluations of the User-based CAC Scheme 77
5.2.1 Evaluations of QoS-Related Terms 78
5.2.2 Impacts of Throttle Threshold on Evaluated Terms 83
5.2.3 Impacts of Channel Holding Time on Evaluated Terms 87
5.3 Performance Evaluations of the Cell-based CAC Scheme 90
5.3.1 Verifications of Prediction Results 90
5.3.2 Evaluations of the proposed CAC 103
5.3.3 Evaluations of Throttle Mechanism 113
CHAPTER 6 CONCLUSIONS AND FUTURE RESEARCHES 119
6.1 Conclusions 119
6.2 Future Researches 122
References 125
[1]V. H. MacDonald, “The cellular concept,” Bell Systems Technical Journal, Vol. 58, No. 1, pp. 15-43, Jan. 1979.
[2]W. R. Young, “Advanced mobile phone service: Introduction, Background, and Objectives,” Bell Systems Technical Journal, Vol. 58, No. 1, pp. 1-14, Jan. 1979.
[3]A. D. Kucar, “Mobile radio: An overview,” IEEE Communication Magazine, Vol. 29, No. 11, pp. 72-85, Nov. 1991.
[4]D. J. Goodman, “Trends in cellular and cordless communications,” IEEE Communication Magazine, Vol. 29, No. 6, pp. 31-39, June 1991.
[5]J. L. Sanvos, “A comparison of binary paging codes,” Proc. of the 32nd IEEE Vehicular Technology Conference, pp. 392-402, May 1982.
[6]A. Maloberti, “Radio transmission interface of the digital Pan European mobile system,” Proc. of the 39th IEEE Conference on Vehicular Technology, pp. 712-717, May 1989.
[7]V. K. Garg and J. E. Wilkes, Principle & Applications of GSM, Prentice Hall, Upper Sadle River, NJ, U.S., 1999.
[8]J. C. Liberti and T. S. Rappaport, Smart Antennas for Wireless Communications: IS-95 and Third Generation Applications, Prentice Hall, Upper Sadle River, NJ, U.S., 1999.
[9]K. I. Kim, Handbook of CDMA System Design, Engineering, and Optimization, Prentice Hall, Upper Sadle River, NJ, U.S., 2000.
[10]V. K. Garg, IS-95 CDMA and cdma2000, Prentice Hall, Upper Sadle River, NJ, U.S., 2000.
[11]F. E. Garcia-Palacios, A. Marshall, and D. McCartan, “Reducing dropping calls for prioritised services in next generation multimedia networks,” Proc. of the First International Conference on 3G Mobile Communication Technologies, pp. 356-360, Mar. 2000.
[12]L. Perato and K. A. Agha, “Handover prediction: user approach versus cell approach,” Proc. of the 4th International Workshop on Mobile and Wireless Communications Network, pp. 9-11 Sept. 2002.
[13]D. Niyato and E. Hossain, “Call admission control for QoS provisioning in 4G wireless networks: issues and approaches,” IEEE Network, Vol. 19, No. 5, pp. 5-11, Sept. 2005.
[14]G. Calhoun, Digital Cellular Radio, Artech House Inc., 1988.
[15]T. S. Rappaport, Wireless Communications Principles and Practice 2ed, Prentice Hall, Upper Sadle River, NJ, U.S., 2000.
[16]K. C. Chua, B. Bensaou, W. Zhuang, and S. Y. Choo, “Dynamic channel reservation (DCR) scheme for handoffs prioritization in mobile micro/pico cellular networks,” Proc. of IEEE International Conference on Universal Personal Communications (ICUPC’98), pp. 383–387, Oct. 1998.
[17]H. Chen, S. Kumar and C.-C. Jay Kuo, “Dynamic call admission control scheme for QoS priority handoff in multimedia cellular system,” Proc. of IEEE Wireless Communications and Networking Conference (WCNC’2002), Vol.1, pp. 114-118, Mar. 2002.
[18]P.-O. Gaasvik, M. Cornefjord, and V. Svensson, “Different methods of giving priority to handoff traffic in a mobile telephone system with directed retry,” Proc. of the 41st IEEE Vehicular Technology Conference ‘Gateway to the Future Technology in Motion’, pp. 549–553, May 1991.
[19]L. T. Lee, C. Y. Tseng, C. F. Wu, C. R. Wei, and D. F. Tao, “An Extenics-based handover algorithm for supporting QoS in wireless cellular network,” Proc. of the 2003 International Conference on Distributed Multimedia Systems, pp. 531-534, Sept. 2003.
[20]L. T. Lee, C. F. Wu, C. R. Wei, and D. F. Tao, “The use of accumulated user mobility for supporting quality of service in mobile cellular systems,” Proc. of the 2004 International Conference on Distributed Multimedia Systems, pp. 375-378, Sept. 2004.
[21]L. T. Lee and C. F. Wu, “An adaptive handoff algorithm with accumulated attempts of user mobility for supporting QoS in wireless cellular networks,” International Journal of Computer Science and Network Security, Vol. 6, No. 9B, pp. 56-62, Sept. 2006.
[22]R. A. Guerin, Ph. D. thesis, Dept. of Electrical Engineering, CA Inst. of Tech., Pasadena, CA., U.S., 1986.
[23]D. Hong and S. S. Rappaport, “Traffic model and performance analysis for cellular mobile radio telephone systems with prioritized and nonprioritized handoff procedures,” IEEE Transactions on Vehicular Technology, Vol. 35, No. 3, pp. 77-92, Aug. 1986.
[24]I. Katzela and M. Naghshineh, “Channel assignment schemes for cellular mobile telecommunication systems: a comprehensive survey,” IEEE Personal Communications, pp. 10-31, June 1996.
[25]M. Zhang and T. P. Yum, “Comparisons of channel-assignment strategies in cellular mobile telephone systems,” IEEE Transactions on Vehicular Technology, Vol. 38, No. 4, pp. 211-215, Nov. 1989.
[26]J. Tajima and K. Imamura, “A strategy for flexible channel assignment in mobile communication system,” IEEE Transactions on Vehicular Technology, Vol. 37, No. 2, pp. 92-103, May 1988.
[27]D. C. Cox and D. O. Reudnik, “Increasing channel occupancy in large-scale mobile radio systems: dynamic channel assignments,” IEEE Transactions on Vehicular Technology, Vol. 22, No. 4, pp. 218-222, Nov. 1973.
[28]M. Zhang and T.-S. Yum, “The nonuniform compact pattern allocation algorithm for cellular mobile systems,” IEEE Transactions on Vehicular Technology, Vol. 40, No. 2, pp. 387-391, May 1991.
[29]S.-H. Oh and D.-W. Tcha, “Prioritized channel assignment in a cellular radio network,” IEEE Transactions on communications, Vol. 40, No. 7, pp. 1259-1269, July 1992.
[30]T. J. Kahwa and N. D. Georganas, “A hybrid channel assignment scheme in large-scale, cellular-structured mobile communication systems,” IEEE Transactions on Communications, Vol. 26, No. 4, pp. 432-438, Apr. 1978.
[31]S. M. Elnoubi, R. Singh, and S. C. Gupta, “A new frequency channel assignment algorithm in high capacity mobile communication systems,” IEEE Transactions on Vehicular Technology, Vol. 31, No. 3, pp. 121-131, Aug. 1982.
[32]K. Okada and F. Kubota, “On dynamic channel assignment strategies in cellular mobile radio systems,” IEICE Transactions on Fundamentals., Vol. 75, pp. 1634-1641, 1992.
[33]R. Beck and H. Panzer, “Strategies for handover dynamic channel allocation in mico-cellular mobile radio telephone systems,” Proc. of the 39th IEEE Vehicular Technology Conference, Vol. 1, pp. 178-185, 1989.
[34]S. Tekinay and B. Jabbari, “Handover and channel assignment in mobile cellular networks,” IEEE Communication Magazine, Vol. 29, No. 11, pp. 42-46, Nov. 1991.
[35]R. A. Gutrin, “Channel occupancy time distribution in a cellular radio system,” IEEE Transactions on Vehicular Technology, Vol. 36, No. 3, pp. 89-99, Aug. 1987.
[36]P. V. Oflik and S. S. Rappaport, “A model for teletraffic performance and channel holding time characterization in wireless cellular communication,” Proc. of IEEE International Conference on Universal Personal Communications (ICUPC’97), pp. 671-675, Oct. 1997.
[37]Y. Fang, I. Chlamtac, and Y.-B. Lin, “Channel occupancy times and handoff rate for mobile computing and PCS networks,” IEEE Transactions on Computers, Vol. 47, No. 6, pp. 679-692, June 1998.
[38]Y.-B. Lin, “Performance modeling for mobile telephone networks,” IEEE Network, Vol. 11, No. 6, pp. 63-67, Nov. 1997.
[39]L. T. Lee, C. F. Wu, C. Y. Tseng, and K. Y. Liu, “A dynamic reservation and call admission control policy with HMM for multimedia cellular networks,” Proc. of IEEE International Symposium on Communications and Information Technologies 2006, W2E-2, Oct. 2006.
[40]L. T. Lee, C. F. Wu, D. F. Tao, and K. Y. Liu, “A cell-based call admission control policy with time series prediction and throttling mechanism for supporting QoS in wireless cellular networks,” Proc. of IEEE International Symposium on Communications and Information Technologies 2006, W2E-3, Oct. 2006.
[41]C. Bettstetter, “Mobility modeling in wireless networks: categorization, smooth movement, and border effect,” ACM SIGMOBILE Mobile Computing and Communications Review, Vol. 5, No. 3, pp. 55-66, July 2001.
[42]M. M. Zonozzi and E Dassanayake, “Mobility modeling and channel holding time distribution in cellular mobile communication systems,” Proc. of IEEE Global Telecommunications Conference (Globecom’95), Vol. 1, pp. 13-17, Nov. 1995.
[43]M. M. Zonoozi and R Dassanayake, “User mobility/modeling and characterization of mobility patterns,” IEEE Journal on Selected Areas in Communications, Vol. 15, No. 7, pp. 1239-1252, Sept. 1997.
[44]S. Nanda, “Teletraffic models for urban and suburband microcells: cell sizes and handoff rates,” IEEE Transactions on Vehicular Technology, Vol. 42, No. 4, pp. 673-682, Nov. 1993.
[45]Z. Lei and C. Rose, “Wireless subscriber mobility management using adaptive individual location areas for PCS systems,” Proc. of IEEE International Conference on Communications (ICC’98), Vol. 3, pp. 1390-1394, June 1998.
[46]Z. Lei and C. Rose, “Probability criterion based location tracking approach for mobility management of personal communication systems,” Proc. of IEEE Global Telecommunications Conference (Globecom’97), pp. 977-981, Nov. 1997.
[47]R. Thomas, H. Gilbert, and G. Mazziotto, “Influence of the moving of the mobile stations on the performance of a radio mobile cellular network,” Proc. of Nordic Seminar on Digital Land Mobile Radio Communications, Copenhagen, Denmark, paper 9.4, Sept. 1988.
[48]B. Jabbari, Y. Zhou, and F. Hillier, “Random walk modeling of mobility in wireless networks,” Proc. of the 48th IEEE Vehicular Technology Conference, Vol. 1, pp. 639-643, May 1998.
[49]A. Bar-Noy, I. Kessler, and M. Sidi, “Mobile users: To update or not to update,” ACM/Baltzer Wireless Networks, Vol. 1, No. 2, pp. 175-185, June 1995.
[50]F. Akyildiz, S. M. Ho, and Y.-B. Lin, “Movement-based location update and selective paging for PCS networks,” IEEE/ACM Transactions on Networking, Vol. 4, No. 4, pp. 629-639, Aug. 1996.
[51]S. Kim, M. Y. Chung, and D. K. Sung, “Mobility and traffic analyses in three-dimensional PCS environments,” IEEE Transactions on Vehicular Technology, Vol. 47, No. 2, pp. 537-545, May 1998.
[52]S. Kim, M. Y. Chung, D. K. Sung, and M. Sengoku, “Mobility and traffic analyses in three-dimensional indoor environments,” IEEE Transactions on Vehicular Technology, Vol. 47, No. 2, pp. 546-557, May 1998.
[53]S. Kim, J. K. Kwon, and D. K. Sung, “Mobility and traffic analysis in three-dimensional highrise building environments,” IEEE Transactions on Vehicular Technology, Vol. 49, No. 5, pp. 1633-1640, May 2000.
[54]M. S. Sricharan, V. Vaidehi, and P. P. Arun, “An activity based mobility prediction strategy for next generation wireless networks,” Proc. of 2006 IFIP International Conference on Wireless and Optical Communications Networks, p. 5, Apr. 2006.
[55]N. Samaan and A. Kormouch, “A mobility prediction architecture based on conceptual knowledge and spatial conceptual maps,” IEEE Transactions on Mobile Computing, Vol. 4, No 6, pp. 537-551, Nov. 2005.
[56]I. F. Akyildiz and W. Wang, “The predictive user mobility profile framework for wireless multimedia networks,” IEEE/ACM Transactions on Networking, Vol. 12, No. 6, pp. 1021–1035, Dec. 2004.
[57]R.Zaidi Zainab and L.Mark Brain, “Real time tracking mobility tracking algorithms for cellular networks based on kalman filtering,” IEEE Transactions on Mobile Computing, Vol. 4, No. 2, pp. 195-208, Mar. 2005.
[58]T. Liu, et al., “Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks,” IEEE Journal on selected areas in Communications, Vol. 16, No. 6, pp. 922-936, Aug. 1998.
[59]X. Shen, J. W. Mark, and J. Ye, “User mobility profile prediction: an adaptive fuzzy inference approach,” Wireless Networks, Vol. 6, No. 5, pp. 363-374, Nov. 2000.
[60]J. Chan, S. Zhou, and A. Seneviratne, “A QoS adaptive mobility prediction scheme for wireless networks,” Proc. of IEEE Global Telecommunications Conference (GLOBECOM’98), Vol. 3, pp. 8-12, Nov. 1998.
[61]F. Yu and V. Leung, “Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks,” Computer Network, Vol. 38, No. 5, pp. 577-589, Apr. 2002.
[62]W. Su, S.-J. Lee, and M. Gerla, “Mobility prediction in wireless networks,” Proc. of the 21st Century Military Communications Conference (MILCOM’2000), Vol. 1, pp. 491-495, Oct. 2000.
[63]A. Quintero, “A user pattern learning strategy for managing users’ mobility in umts networks,” IEEE Transactions On Mobile Computing, Vol. 4, No. 6, pp. 552-556, Nov. 2005.
[64]W. Ma and Y. Fang, “A new location management strategy based on user mobility pattern for wireless networks,” Proc. of the 27th Annual IEEE Conference on Local Computer Networks (LCN’02), pp. 451-457, Nov. 2002.
[65]R. Jain and E. W. Knightly, “A framework for design and evaluation of admission control algorithms in multi-service mobile networks,” Proc. of the 8th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’99), Vol. 3, pp. 1027–35, Mar. 1999.
[66]R. Ramjee, R. Nagarajan, and D. Towsley, “On optimal call admission control in cellular networks,” Proc. of the 5th Annual Joint Conference of the IEEE Computer Societies, Networking the Next Generation (INFOCOM’96), Vol. 1, pp. 43–50, Mar. 1996.
[67]B. Epstein and M. Schwartz, “Predictive QoS-based admission control for multiclass traffic in cellular wireless networks,” IEEE Journal on selected areas in Communications, Vol. 18, No. 3, pp. 523–34, Mar. 2000.
[68]T. Zhang et al., “Local predictive resource reservation for handoff in multimedia wireless IP networks,” IEEE Journal on selected areas in Communications, Vol. 19, No. 10, pp. 1931–41, Oct. 2001.
[69]D. A. Levine, I. F. Akyildiz, and M. Naghshineh, “A resource estimation and call admission algorithm for wireless multimedia networks using the shadow cluster concept,” IEEE/ACM Transactions on Networking, Vol. 5, No. 1, pp. 1–12, Feb. 1997.
[70]J. Hou, J. Yang, and S. Papavassiliou, “Integration of pricing with call admission control to meet QoS requirements in cellular networks,” IEEE Transactions on Parallel and Distributed System, Vol. 19, No. 9, pp. 898–910, Sept. 2002.
[71]J. Tsiligaridis and R. Acharya, “A clustering prediction scheme for wireless cellular network,” Proc. of the 2005 International Symposium on Collaborative Technologies and Systems, pp. 298-304, May 2005.
[72]J.-J. Won, E.-S. Hwang, H.-W Lee, and C.-H. Cho, “Mobile cluster based call admission control in wireless mobile networks,” Proc. of the 57th IEEE Semiannual Vehicular Technology Conference (VTC 2003-Spring), Vol. 3, pp. 1527-1531, Apr. 2003.
[73]R. Dugad and U. B. Desai, “A tutorial on hidden Markov Models,” Technical Report No.: SPANN-96.1 Signal Processing and Artificial Neural Networks Lab. Department of Electrical Engineering Indian Institute of Technology, pp. 1-16, May 1996.
[74]M. A. Mohamed and P. Gader, “Generalized hidden Markov models—Part I: Theoretical frameworks,” IEEE Transactions on Fuzzy Systems, Vol. 8, No. 1, pp. 67-81, Feb. 2000.
[75]L. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proc. of the IEEE, Vol. 77, No. 2, pp. 257–286, Feb. 1989.
[76]H. Yang and K. Alman, “2-D shape classification using hidden Markov model,” IEEE Transactions on Pattern Analysis Machine Intelligence, Vol. 13, No. 11, pp. 1172–1184, Nov. 1991.
[77]M. A. Mohamed and P. D. Gader, “Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques,” IEEE Transactions on Pattern Analysis Machine Intelligence, Vol. 18, No. 5, pp. 548–554, May 1996.
[78]L. R. Rabiner and B. H. Juang, “An introduction to hidden Markov models,” IEEE ASSP Magazine, Vol. 3, No. 1, pp. 4-16, Jan. 1986.
[79]M. H. MacDougall, Simulating Computer Systems Techniques and Tools, MIT Press, Cambridge, Massachusetts London, England, 1987.
[80]A. Gurijala and C. Molina, “Defining and monitoring QoS metrics in the next generation wireless networks,” Proc. of IEE Telecommunications Quality of Services: The Business of Success, pp. 37–42, Mar. 2004.
[81]J. Misic and T. Y. Bun, “On call level QoS guarantees under heterogeneous user mobilities in wireless multimedia networks,” Proc. of IEEE Global Telecommunications Conference (GLOBCOM’99), Vol. 5, pp. 2730-2736, 1999.
[82]K. L. Yeung and S. Nanda, “Channel management in micro/macrocell cellular radio systems,” IEEE Transactions on Vehicular Technology, Vol. 45 No. 4, pp. 601-612, Nov. 1996.
[83]W. William and S. Wei, Time Series Analysis: Univariate and Multivariate Methods, Addison Wesley Publication, 1999.
[84]T. Koskela, M. Varsta, J. Heikkonen, and K. Kaski, “Time series prediction using recurrent SOM with local linear models,” Research Report B15, Helsinki University of Technology, Laboratory of Computational Engineering, Finland, 1997.
[85]S. Kamitsuji and R. Shibata, “Effectiveness of stochastic neural network for prediction of fall or rise of TOPIX,” Research Paper, Keio University, 2004.
[86]N. Kasabov, Evolving Connectionist Systems, Springer-Verlag London Limited, 2003.
[87]O. B. Yaik, C. H. Yong, and F. Haron, “Time series prediction using adaptive association rules,” Proc. of the First International Conference on Distributed Frameworks for Multimedia Applications (DFMA’05), pp. 310-314, Feb. 2005.
[88]L. M. Lamsade and J. B.-O. Prism, “A new tool for performance evaluation for wireless networks,” Proc. of the 10th International Conference on Telecommunications (ICT’2003), Vol. 1, pp. 39-44, Mar. 2003.
[89]J. Misra, “Distributed discrete-event simulation,” Computing Surveys, Vol. 18, No. 1, pp. 39-65, Mar. 1986.
[90]A. Boukerche, S. Hong and T. Jacob, “A performance study of a distributed algorithm channel allocation,” Proc. of the 3rd ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems, pp. 36-43, Aug. 2000.
[91]J. A. Payne, Introduction to simulation, McGraw-Hill, 1982.
[92]A. Thesen and L. E. Travis, Simulation for decision making, West Publishing Co., 1992.
[93]O. Castillo and P. Melin, “Automated mathematical modelling for financial time series prediction using fuzzy logic, dynamical systems and fractal theory,” Proc. of the IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, pp. 120-126, Mar. 1996.
[94]D. Coyle, G. Prasad, and T. M. McGinnity, “Improving signal separability and inter-session stability for a brain-computer interface by time-series-prediction-preprocessing,” Proc. of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 5412-5415, Sept. 2005.
[95]D. Coyle, G. Prasad, and T. M. McGinnity, “A time-series prediction approach for feature extraction in a brain-computer interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 13, No. 4, pp. 461–467, Dec. 2005.
[96]M. Hasegawa, G. Wu, and M. Mizuni, “Applications of nonlinear prediction methods to the Internet traffic,” Proc. of the 2001 IEEE International Symposium on Circuits and Systems, Vol. 2, pp. 169-172, May 2001.
[97]L. T. Lee, H. Y. Chang, and C. H. Liang, “An adaptive scheme of task scheduling in grid computing environments,” Proc. of International Conference on Network and Parallel Computing (IFIP’06), pp. 121-127, Oct. 2006.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top