研究生: |
許純修 CHUN-XIU HSU |
---|---|
論文名稱: |
具有通道組合與感測錯誤之多重速率感知無線電網路分析 Analysis of Multi-rate Cognitive Radio Networks with Channel Assembling and Sensing Errors |
指導教授: |
鍾順平
Shun-Ping Chung |
口試委員: |
王乃堅
Nai-Jian Wang 林永松 Yeong-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: | 2015 |
畢業學年度: | 103 |
語文別: | 英文 |
論文頁數: | 183 |
中文關鍵詞: | 感知無線電網路 、連結允入控制 、通道組合 、通道捐贈 、通道重新分配 、錯誤偵測 、虛假警報 、成功送達率 、阻塞機率 |
外文關鍵詞: | Cognitive radio networks, call admission control, channel assembling, channel donation, channel reallocation, misdetection, false alarm, throughput, blocking probability |
相關次數: | 點閱:427 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 |
許多研究指出在許多領域中,分配的頻譜有絕大部分很少被充分利用,像是時域、頻域和地理位置。頻譜的低利用率揭示了頻寬資源的稀少性對於現今4G與未來網路形成挑戰。諸如感測網路和智慧型手機,這類的無線用戶及應用的快速興起,傳統的頻譜分配方法再也無法滿足現今頻譜日益增加的需求。為了減緩頻譜低利用率的問題,感知無線電網路已經被提出。我們考慮了多重速率的感知無線電網路,在此網路中的不同類別用戶有不同的頻寬需求。在感知無線電網路中,存在著兩種類別的用戶:主要用戶(PU)和次要用戶(SU)。主要用戶一次使用一個通道,然而次要用戶則是依據連結允入控制(CAC)來決定使用多少通道。我們研究了三種連結允入控制策略,分別為CAC1、CAC2和CAC3。在三個我們考慮的連結允入控制策略中,主要用戶對於次要用戶擁有進接通道的優先權,它可以在任何時候占用由次要用戶正在使用的通道。在CAC1中,通道組合策略並沒有被次要用戶採用,然而,在CAC2 和CAC3中次要用戶則使用了通道組合策略。特別是,在CAC1中次要用戶的頻寬需求為一個通道,在CAC2中次要用戶的頻寬需求則是固定數量通道,而在CAC3中次要用戶的頻寬需求則是介於一上下限之間。當一個次要使用者欲進接系統時,如果情況需要的話,通道捐贈會被啟動。當任何一次要用戶被一個剛抵達的主要用戶所占先時,次要用戶將會試圖做系統內交遞。在CAC1和CAC2中,一個未成功的系統內交遞會迫使次要用戶的傳輸中斷及離開系統。在CAC3中,一個未成功的系統內交遞會使得次要用戶比被佔先前少占用一個通道。當任何類別使用者完成服務時,如果情況需要的話,通道重新分配會被啟動。我們也考慮了兩種情境:無錯誤情境和有錯誤情境。在有錯誤情境中,存在著兩種感測錯誤:錯誤偵測及虛假警報。針對所有考慮的連結允入控制策和情境,我們開發了對應的解析模型。我們研究不同系統參數對於各種效能指標的影響。這些我們感興趣的效能指標有主要用戶/次要用戶的成功送達率、主要用戶/次要用戶的資源利用率、主要用戶/次要用戶的阻塞機率、次要用戶中斷機率。最後但不是最不重要的,我們使用visual C++來撰寫電腦模擬以驗證解析結果的準確性。
Studies have shown that large portions of the allocated spectrum are rarely fully utilized in different domains, e.g., time, frequency, and geographical location. Spectral under-utilization reveals the spectrum scarcity challenge inherently in 4G and beyond. Due to the rapid growth of wireless users and applications, such as sensor networks and smart phones, traditional spectrum allocation strategies cannot meet today’s increasing requirement of radio resources. As the solution to alleviate the problem of low spectral usage, Cognitive Radio Networks (CRNs) have been proposed. We consider the multirate cognitive radio networks where the users of each class may have a different bandwidth requirement. There are two classes of users: primary users (PU), and secondary users (SU). The bandwidth requirement of a PU call is one channel, whereas that of an SU call depends on the call admission control (CAC) used. We study three CAC schemes: CAC1, CAC2, and CAC3. In three CACs considered, as far as new calls are concerned, PU calls have the preemptive priority over SU calls to access the channels and reclaim channels being used by SU calls at any time. In CAC1, no channel assembling is utilized for SU calls, whereas in CAC2 and CAC3 channel assembling is utilized for SU calls. Specifically, the bandwidth requirement of an SU call with CAC1 is one channel, that with CAC2 is a constant number of channels, that with CAC3 is between a lower bound and an upper bound. When a SU new call arrives, channel donation is enforced if necessary. When any SU is preempted by any PU arrival, it will attempt to perform intra-handoff. In CAC1 and CAC2, an unsuccessful intra-handoff SU call will be dropped from the system and forcefully terminated. In CAC3, an unsuccessful intra-handoff SU call will occupy one less channel than that before it is preempted. When any call completes the service, channel reallocation is enforced if necessary. We also consider two scenarios: error-free and error-prone. In error-prone scenarios there are two classes of errors: misdetection (MD) and false alarm (FA). We develop the analytical models for all CAC schemes and scenarios considered. We also investigate the effect of various system parameters on the performance measures. The performance measures of interest are PU/SU throughput, PU/SU utilization, PU/SU blocking probability, and SU forced termination probability. Last but not least, computer simulation is written in visual C++ to verify the accuracy of the analytical results.
[1]G. Staple and K. Werbach, “The end of spectrum scarcity,” IEEE Spectrum, vol. 41, pp. 48–52, Mar. 2004.
[2]Federal Communications Commission, “Spectrum policy task force report,” Tech. Rep. (ET Docket No. 02-135), available online: http://hraunfoss.fcc.gov/edocs public/attachmatch/DOC-228542A1.pdf, Nov 2002.
[3]J.Mitola III, “Cognitive radio: An integrated agent architecture for software Defined Radio,” Ph.D Dissertation, KTH, Stockholm, 2000.
[4]S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE JSAC, vol. 23, pp. 201-220, Feb. 2005.
[5]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.
[6]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.
[7]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.
[8]S. A. Jafar and S. Srinivasa, “Capacity limits of cognitive radio with distributed and dynamic spectral activity,” IEEE JSAC., vol. 25, no. 3, pp. 529-537, Apr. 2007.
[9]B. Wang, Z. Ji, K. J. R. Liu, and T. C. Clancy, “Primary-prioritized markov approach for dynamic spectrum allocation,” IEEE Trans. Wireless Commun., vol. 8, no. 4, pp. 1854-1865, Apr. 2009.
[10]J. Lee and J. So, “Analysis of cognitive radio networks with channel aggregation,” Proc. IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6, Apr. 2010.
[11]L. Jiao, V. Pla, and F. Y. Li, “Analysis of channel bonding/aggregation for multi-channel cognitive radio networks,” Proc. 16th European Wireless Conference, pp. 468-474, Apr. 2010.
[12]X. Zhu, L. Shen, and T.-S. P. Yum, “Analysis of cognitive radio spectrum access with optimal channel reservation,” IEEE Commun. Lett., vol. 11, no. 4, pp. 304-306, Apr. 2007.
[13]S. M. Kannapa and M. Saquib, “Performance analysis of cognitive network with dynamic spectrum assignment to secondary users,” Proc. IEEE International Conference on Communications (ICC), pp. 1-5, May 2010.
[14]T. M. N. Ngatched, S. Dong, A. S. Alfa, and J. Cai, “Performance analysis of cognitive radio networks with channel assembling and imperfect sensing,” Proc. IEEE International Conference on Communications (ICC), pp. 1688-1692, Jun. 2012.
[15]S. Tang and B. Mark, “Modeling and Analysis of Opportunistic Spectrum Sharing with Unreliable Spectrum Sensing,” IEEE Trans. Wireless Comm., vol. 8, no. 4, pp. 1934-1943, Apr. 2009.