簡易檢索 / 詳目顯示

回結果列表
研究生： 邱浩倫
Hau-luen Chiou
論文名稱： 支援通道集成之多重速率感知無線電細胞式網路效能評估
Performance Evaluation of Multirate Cognitive Radio Cellular Networks with Channel Aggregation
指導教授： 鍾順平
Shun-Ping Chung
口試委員: 王乃堅
Nai-Jian Wang
林永松
Yung-Sung Lin
學位類別： 碩士
Master
系所名稱： 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 124
中文關鍵詞： 多重速率 、感知無線電 、通道集成 、細胞內交遞 、連結阻塞機率 、資源利用率
外文關鍵詞： multirate, channel aggregation, intra-handoff call
相關次數： 點閱：364  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 由於傳統的靜態頻率分配策略，今日的頻譜在不同域很少被充分利用，像是時域、頻域和地理位置。由於無線的用戶和應用快速成長，頻譜的欠利用與無線資源需求的不斷增加形成對比。為了減緩這個問題，感知無線電已經被提出。在一個感知無線電網路裡，存在著兩種類別的用戶：次級用戶(SU)和主要用戶(PU)。只要不會對主要戶造成有害干擾，次級用戶可以使用授權頻譜。明確地說，如果一個通道沒有被主要戶所使用，次級用戶則可以使用這通道。為了保護主要用戶的傳輸，當它要再次使用此通道時，次級用戶必須要立即釋出此通道。然而，這個保護主要用戶的機制顯著的影響了次級用戶的效能。為了增加彈性，次級用戶可以集成許多子通道為一個通道來取得較好的服務。我們考慮多重速率感知無線電細胞式網路，其中每一種類別的使用者有著不同頻寬需求。我們研究兩種連結允入控制(CAC)策略：CAC1和CAC2。在CAC1中，次級用戶沒有利用通道集成的策略，而在CAC2中，次級用戶有使用通道集成的策略。主要用戶的頻寬需求為一個通道，而次級用戶的頻寬需求根據CAC而有所不同。明確地說，在CAC1中,次級用戶的頻寬需求為一個子通道，而在CAC2中，次級用戶的頻寬需求是介於一個上下限之中。在這兩個考慮的CAC中，無論是新連結或細胞間交遞連結，主要用戶對於次級用戶都有使用通道的佔先優先權。當任一個次級用戶被一個剛抵達的主要用戶所佔先時，次級用戶將會試圖做細胞內交遞。一個未成功的細胞內交遞使得次級用戶的傳輸被迫中斷。為了提供優先權給交遞連結，我們採用保護通道機制。我們將所考慮的系統塑模成為一個多維馬可夫程序。我們提出一個疊代演算法去找出穩態機率分佈和計算感興趣的效能指標。這些效能指標是新連結和細胞間交遞的連結阻塞機率、資源利用率和服務完成率。我們研究不同系統參數對於各種效能指標的影響。最後但不是最不重要的，電腦模擬被用來驗證數學解析結果的準確性。

    關鍵字：多重速率、感知無線電、通道集成、細胞內交遞、連結阻塞機率、資源利用率

    With the traditional static frequency allocation policy, today’s spectrum is rarely fully utilized in different domains, e.g., time, frequency, and geographical location. Spectral under-utilization is contrasting the increasing demand for radio resources, due to the rapid growth of wireless users and applications. To alleviate the problem, Cognitive Radio (CR), has been proposed. In CR networks, there are two classes of users: secondary (SU) and primary (PU). SUs can use licensed spectrum as long as they do not harmfully interfere with PUs. Specifically, if and when a channel is not used by the PU, the channel is available for the SU. To protect the PU transmission, the SUs must vacate accessed channels immediately if the PUs start using them again. The PU protection mechanism, however, significantly affects the transmission performance of SUs. To increase flexibility, SUs may aggregate several sub-channels together as one channel to acquire better service. We consider the multirate cognitive radio cellular networks where the users of each class have a different bandwidth requirement. We study two call admission control (CAC) policies: CAC1 and CAC2. In CAC1, no channel aggregation is utilized for SU calls, whereas in CAC2 channel aggregation is utilized for SU calls. The bandwidth requirement of a PU call is one channel. The bandwidth requirement of an SU call depends on the CAC used. Specifically, the bandwidth requirement of an SU call with CAC1 is one sub-channel, whereas that with CAC2 is between a lower bound and an upper bound. In both CACs considered, for both new and inter-handoff calls, PUs have the preemptive priority of accessing the channels over SUs. When any SU is preempted by any PU arrival, it will attempt to perform intra-handoff. An unsuccessful intra-handoff makes SU’s transmission be forcefully terminated. To provide handoff calls priority over new calls, the guard channel mechanism is enforced. We model the system considered as a multi-dimensional Markov process. We propose an iterative algorithm to find the steady state probability distribution and compute the performance measures. The performance measures of interest are new/inter-handoff call blocking probability, utilization, and service-completion rate. We study the effect of different system parameters on performance measures. Last but not least, computer simulation is used to verify the accuracy of the analytical result.

    Key words: multirate, cognitive radio, channel aggregation, intra-handoff call, blocking probability, utilization

    CONTENTS 摘要 I ABSTRACT II CONTENTS III Contents of Tables V Contents of Figures V 1.Introduction 1 2. System Model 3 2.1 CAC1 4 2.2 CAC2 4 3. Analytical Model 7 3.1 CAC1 7 3.1.1 Equilibrium Equations 7 3.1.2 Steady State Probability 11 3.1.3 Performance Measures 11 3.2 CAC2 13 3.2.1 Equilibrium Equations 13 3.2.2 Steady State Probability 15 3.2.3 Performance Measures 15 4. Simulation Model 25 4.1 PU New Arrival 25 4.1.1 Intra-Handoff Process 25 4.2 SU New Arrival 26 4.3 PU/SU Inter-Handoff Arrival 27 4.3.1 CAC1 27 4.3.2 CAC2 27 4.4 Departure 28 4.5 Performance Measure 29 5. Numerical Results 42 5.1 Multi-Cell Scenarios with Guard Channels 42 5.1.1 PU New Call Arrival Rate 42 5.1.2 SU New Call Arrival Rate 45 5.1.3 PU Service Rate 47 5.1.4 SU Service Rate 50 5.1.5 PU Dwell Rate 52 5.1.6 SU Dwell Rate 55 5.2 Multi-Cell Scenarios without Guard Channels 58 5.2.1 PU New Call Arrival Rate 58 5.2.2 SU New Call Arrival Rate 61 5.2.3 PU Service Rate 63 5.2.4 SU Service Rate 66 5.2.5 PU Dwell Rate 68 5.2.6 SU Dwell Rate 71 6. Conclusions 122 REFERENCES 123   List of Tables Table 3-1 17 Table 3-2 System parameters 23 List of Figures Fig. 2-1 Spectrum partition 3 Fig. 3-1 State transitions for CAC1 24 Fig. 4-1 Main program for CAC1 and CAC2 30 Fig. 4-2 Initialization for CAC1 31 Fig. 4-3 Initialization for CAC2 32 Fig. 4-4 Timing for CAC1 and CAC2 33 Fig. 4-5 PU new arrive mechanism for CAC1 34 Fig. 4-6 PU new arrive mechanism for CAC2 35 Fig. 4-7 SU new arrive mechanism for CAC1 36 Fig. 4-8 SU new arrive mechanism for CAC2 37 Fig. 4-9 Handoff mechanism for CAC1 38 Fig. 4-10 Handoff mechanism for CAC2 39 Fig. 4-11 PU and SU departures for CAC1 40 Fig. 4-12 PU and SU departures for CAC2 41 Fig. 5 1: PU New call blocking probability vs. PU new call arrival rate 74 Fig. 5-2 SU New call blocking probability vs. PU new call arrival rate 74 Fig. 5-3: PU Inter-handoff call blocking probability vs. PU new call arrival rate 75 Fig. 5 4: SU inter-handoff call blocking probability vs. PU new call arrival rate 75 Fig. 5 5: PU utilization vs. PU new call arrival rate 76 Fig. 5 6: SU utilization vs. PU new call arrival rate 76 Fig. 5 7: PU service-completion rate vs. PU new call arrival rate 77 Fig. 5 8: PU service-completion rate vs. PU new call arrival rate 77 Fig. 5-9: PU New call blocking probability vs. SU new call arrival rate 78 Fig. 5-10: SU New call blocking probability vs. SU new call arrival rate 78 Fig. 5 11: PU Inter-handoff call blocking probability vs. SU new call arrival rate 79 Fig. 5 12: SU Inter-handoff call blocking probability vs. SU new call arrival rate 79 Figure 5-13: PU utilization vs. SU new call arrival rate 80 Fig. 5-14: SU utilization vs. SU new call arrival rate 80 Fig. 5-15: PU service-completion rate vs. SU new call arrival rate 81 Fig. 5-16: SU service-completion rate vs. SU new call arrival rate 81 Fig. 5-17: PU New call blocking probability vs. PU service rate 82 Fig. 5-18: SU New call blocking probability vs. PU service rate 82 Fig. 5-19: PU Inter-handoff call blocking probability vs. PU service rate 83 Fig. 5-20: SU Inter-handoff call blocking probability vs. PU service rate 83 Fig. 5-21: PU utilization vs. PU service rate 84 Fig. 5-22: SU utilization vs. PU service rate 84 Fig. 5-23: PU service-completion rate vs. PU service rate 85 Fig. 5-24: SU service-completion rate vs. PU service rate 85 Fig. 5-25: PU New call blocking probability vs. SU service rate 86 Fig. 5-26: SU New call blocking probability vs. SU service rate 86 Fig. 5-27: PU Inter-handoff call blocking probability vs. SU service rate 87 Fig. 5-28: SU Inter-handoff call blocking probability vs. SU service rate 87 Fig. 5-29: PU utilization vs.SU service rate 88 Fig. 5-30: SU utilization vs.SU service rate 88 Fig. 5-31: PU service-completion rate vs. SU service rate 89 Fig. 5-32: SU service-completion rate vs. SU service rate 89 Fig. 5-33: PU New call blocking probability vs. PU dwell rate 90 Fig. 5-34: SU New call blocking probability vs. PU dwell rate 90 Fig. 5-35: PU Inter-handoff call blocking probability vs. PU dwell rate 91 Fig. 5-36: SU Inter-handoff call blocking probability vs. PU dwell rate 91 Fig. 5-37: PU utilization vs. PU dwell rate 92 Fig. 5-38: SU utilization vs. PU dwell rate 92 Fig. 5-39: PU service-completion vs. PU dwell rate 93 Fig. 5-40: SU service-completion vs. PU dwell rate 93 Fig. 5-41: PU New call blocking probability vs. SU dwell rate 94 Fig. 5-42: SU New call blocking probability vs. SU dwell rate 94 Fig. 5-43: PU Inter-handoff call blocking probability vs. SU dwell rate 95 Fig. 5-44: SU Inter-handoff call blocking probability vs. SU dwell rate 95 Fig. 5-45: PU utilization vs. SU dwell rate 96 Fig. 5-46: SU utilization vs. SU dwell rate 96 Fig. 5-47: PU service-completion vs. SU dwell rate 97 Fig. 5-48: SU service-completion vs. SU dwell rate 97 Fig. 5-49: PU New call blocking probability vs. PU new call arrival rate 98 Fig. 5-50: SU New call blocking probability vs. PU new call arrival rate 98 Fig. 5-51: PU Inter-handoff call blocking probability vs. PU new call arrival rate 99 Fig. 5-52: SU inter-handoff call blocking probability vs. PU new call arrival rate 99 Fig. 5-53: PU utilization vs. PU new call arrival rate 100 Fig. 5-54: SU utilization vs. PU new call arrival rate 100 Fig. 5-55: PU service-completion rate vs. PU new call arrival rate 101 Fig. 5-56: PU service-completion rate vs. PU new call arrival rate 101 Fig. 5-57: PU New call blocking probability vs. SU new call arrival rate 102 Fig. 5-58: SU New call blocking probability vs. SU new call arrival rate 102 Fig. 5-59: PU Inter-handoff call blocking probability vs. SU new call arrival rate 103 Fig. 5-60: SU Inter-handoff call blocking probability vs. SU new call arrival rate 103 Fig. 5-61: PU utilization vs. SU new call arrival rate 104 Fig. 5-62: SU utilization vs. SU new call arrival rate 104 Fig. 5-63: PU service-completion rate vs. SU new call arrival rate 105 Fig. 5-64: SU service-completion rate vs. SU new call arrival rate 105 Fig. 5-65: PU New call blocking probability vs. PU new call service rate 106 Fig. 5-66: SU New call blocking probability vs. PU new call service rate 106 Fig. 5-67: PU Inter-handoff call blocking probability vs. PU new call service rate 107 Fig. 5-68: SU Inter-handoff call blocking probability vs. PU new call service rate 107 Fig. 5-69: PU utilization vs. PU new call service rate 108 Fig. 5-70: SU utilization vs. PU new call service rate 108 Fig. 5-71: PU service-completion rate vs. PU new call service rate 109 Fig. 5-72: SU service-completion rate vs. PU new call service rate 109 Fig. 5-73: PU New call blocking probability vs. SU new call service rate 110 Fig. 5-74: SU New call blocking probability vs. SU new call service rate 110 Fig. 5-75: PU Inter-handoff call blocking probability vs. SU new call service rate 111 Fig. 5-76: SU Inter-handoff call blocking probability vs. SU new call service rate 111 Fig. 5-77: PU utilization vs.SU new call service rate 112 Fig. 5-78: SU utilization vs.SU new call service rate 112 Fig. 5-79: PU service-completion rate vs. SU new call service rate 113 Fig. 5-80: SU service-completion rate vs. SU new call service rate 113 Fig. 5-81: PU New call blocking probability vs. PU dwell rate 114 Fig. 5-82: SU New call blocking probability vs. PU dwell rate 114 Fig. 5-83: PU Inter-handoff call blocking probability vs. PU dwell rate 115 Fig. 5-84: SU Inter-handoff call blocking probability vs. PU dwell rate 115 Fig. 5-85: PU utilization vs. PU dwell rate 116 Fig. 5-86: SU utilization vs. PU dwell rate 116 Fig. 5-87: PU service-completion vs. PU dwell rate 117 Fig. 5-88: SU service-completion vs. PU dwell rate 117 Fig. 5-89: PU New call blocking probability vs. SU dwell rate 118 Fig. 5-90: SU New call blocking probability vs. SU dwell rate 118 Fig. 5-91: PU Inter-handoff call blocking probability vs. SU dwell rate 119 Fig. 5-92: SU Inter-handoff call blocking probability vs. SU dwell rate 119 Fig. 5-93: PU utilization vs. SU dwell rate 120 Fig. 5-94: SU utilization vs. SU dwell rate 120 Fig. 5-95: PU service-completion vs. SU dwell rate 121 Fig. 5-96: SU service-completion vs. SU dwell rate 121

    [1] FCC Spectrum Policy Task Force, “Report of The Spectrum Efficiency Working Group,” Technical Report 02-135, Federal Communications Commission, Washington, D.C., 2002.
    [2] J. Mitola III, “Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio,” Ph.D. Dissertation, KTH, Stockholm, 2000.
    [3] Q. Zhao, L. Tong, A. Swami, and Y. Chen “Decentralized Cognitive MAC for Opportunistic Spectrum Access in Ad Hoc Networks: A POMDP Framework,” IEEE JSAC, vol. 25, no. 3, pp. 589-600, Apr. 2007.
    [4] R. Khalona and K. Stanwood, “Channel Aggregation Summary,” IEEE P802.22 W,https://mentor.ieee.org/802.22/dcn/06/22-06-0204-00-0000-channel-aggregation-summary.ppt.
    [5] J. Jia, Q. Zhang, and X. Shen, “HC-MAC: A Hardware-Constrained Cognitive MAC for Efficient Spectrum Management,” IEEE JSAC, vol. 26, no. 1, pp. 106-117, Jan. 2008.
    [6] H. A. B. Salameh, M. M. Krunz, and O. Younis, “MAC Protocol for Opportunistic Cognitive Radio Networks with Soft Guarantees,” IEEE Trans. Mobile Computing, vol. 8, no. 10, pp. 1339 - 1352, Oct. 2009.
    [7] X. Zhu, L. Shen, and T.-S. P. Yum, “Analysis of Cognitive Radio Spectrum Access with Optimal Band Reservation,” IEEE Communications Letters, vol. 11, no. 4, pp. 304-306, Apr. 2007.
    [8] W. Ahmd, J. Gao, H.A. Suraweera, and M. Faulkner, “Comments on Analysis of Cognitive Radio Spectrum Access with Optimal Band Reservation,” IEEE Transactions on Wireless Communications, vol. 8, no. 9, pp. 4488-4491, Sep. 2009.
    [9] Y. Zhang, “Dynamic Spectrum Access in Cognitive Radio Wireless Network” IEEE International Conference on Communications, Beijing, China, pp. 4927-4932, May. 2008.
    [10] E.W.M. Wong and C.H. Foh, “Analysis of Cognitive Radio Spectrum Access with Finite User Population,” IEEE Communications Letters, vol.13, no. 5, pp. 294-296, May. 2009.
    [11] Yong Yao, S.R. Ngoga, D. Erman, A. Popescu, “Performance of Cognitive Radio Spectrum Access with Intra- and Inter-handoff,” IEEE International Conference on Communications, pp. 1539-1544, 2012.
    [12] L. Jiao, V. Pla, and F. Y. Li, “Analysis on Channel Bonding/Aggregation for Multi-channel Cognitive Radio Network,” Proc. European Wireless, Lucca, Italy, pp. 468-474, Apr. 2010.
    [13] L. Jiao, F.Y. Li, and V. Pla, “Dynamic Channel Aggregation Strategies in Cognitive Radio Networks with Spectrum Adaptation,” IEEE Globecom, pp. 1-6, 2011.

    QR CODE