一個在感知無線電網路中分散式的宣告校準方法

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：21

、訪客IP：3.145.91.152

姓名

黃信發(Shin-Fa Huang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

一個在感知無線電網路中分散式的宣告校準方法
(A Distributed Declaration Calibration Scheme in Cognitive Radio Networks)

相關論文

★ 基於OP-TEE的可信應用程式軟體生態系統	★ SeFence: 基於安全感測的可信任周邊存取控制
★ 高解析度二維地理影像的三維建模：旋轉變換投影與傳統方法的比較研究	★ 在低軌道衛星無線通訊中的CSI預測方法
★ 為多流量低軌道衛星系統提出的動態換手策略	★ 基於Trustzone的智慧型設備語音隱私保護系統
★ 一種減輕LEO衛星網路干擾的方案	★ TruzGPS:基於TrustZone的位置隱私權保護系統
★ 衛星地面整合網路之隨機接入前導訊號設計與偵測	★ SatPolicy: 基於Trustzone的衛星政策執行系統
★ TruzMalloc: 基於TrustZone 的隱私資料保護系統	★ 衛星地面網路中基於物理層安全的CSI保護方法
★ 低軌道衛星地面整合網路之安全非正交多重存取傳輸	★ 低軌道衛星地面網路中的DRX機制設計
★ 衛星地面整合網路之基於集合系統的前導訊號設計	★ 基於省電的低軌衛星網路路由演算法

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)

感知無線電網路（Cognitive Radio Networks）的概念要能夠實行，頻譜感測是一個重要的關鍵技術。感知無線電網路系統中的次要使用者（Secondaryusers, SUs），經由頻譜感測所得到的主要使用者（Primary users, PUs）是否正在使用頻譜的資訊，能夠機會式使用頻譜，然而一般而言，SU 並不能夠同時傳送資料以及作感測，因此使用頻譜機會的效率便會降低，也因為現今頻譜感測的硬
體技術尚未成熟，令感知無線電網路能夠較快實行且不降低SU 使用頻譜機會的作法是使用無線頻譜感測器網路（WSSNs）。然而在大自然中存在訊號衰弱（shadowing fading）、多重路徑（multi-path effects）等現象，使得頻譜感測的結果有可能有錯誤產生，因此在本篇論文中，我們利用偵測PU 所在的方位，能夠得知是否有頻譜感測錯誤的節點在附近，進而推演出了一個分散式的宣告校準頻譜感測結果的方法，並且調整了舊有的服務模型，使其作法更貼近現實，並且能更快實行，根據實驗結果，我們得到一個不錯的結果，相信這對未來感知無線電網路真正的實行有所幫助，也給無線頻譜感測器網路系統帶來一個不同的思維。

摘要(英)

Cognitive Radio Networks(CRNs) will be enable to implement, spectrum sensing is a important key technology. Secondary Users(SUs) in cognitive radio are opportunistic allowed to access the spectrum when it get the information about whether Primary Users(PUs) are using the spectrum or not by sensing spectrum. In general, SUs cannot support simultaneous sensing and accessing spectrum. Therefore, the efficiency of utilizing the spectrum opportunities will be degraded. Also because spectrum sensing hardware isn’t mature yet. Using dedicated Wireless Spectrum Sensor Networks (WSSNs) is a way that can let cognitive radio faster to carry out and don’t degrade the efficiency of utilizing the spectrum opportunities. However there is shadowing, multi-path effects and other phenomenon in nature, it may cause result of spectrum sensing error. In this thesis, we exploit detecting the direction of PU, it can find out whether it has the node which has sensing error are in nearby. Furthermore deduce a Distributed Declaration Calibration Scheme to correct the spectrum sensing error and adjust the old service model, let its service model more close to reality and
faster to implement. As the result of experiment, we get a good result, believing that is helpful for real cognitive radio implement. It also brings out a different thought for
WSSNs.

關鍵字(中)

★ 無線感測網路
★ 頻譜偵測
★ 分散式演算法
★ 感知無線電網路

關鍵字(英)

★ Wireless Sensor Networks
★ spectrum sensing
★ distributed algorithm
★ Cognitive Radio Networks

論文目次

Chapter 1 Introduction 1
Chapter 2 Related work 5
Chapter 3 Preliminary 8
3.1 System model 8
3.2 Sensing model 9
3.3 Detection signal determine Model 10
3.4 Key challenges 11
Chapter 4 Distributed Declaration Calibration Scheme 14
4.1 Design principle 14
4.2 Distributed Declaration Calibration Scheme 15
4.2.1 Main idea 15
4.2.2 The proposed scheme 19
4.2.3 Phase 1: SNR vector and exception pair declaration. 19
4.2.4 Phase 2: Estimate 19
4.2.6 Phase 3.2: Faulty nodes correction 23
4.3 Exception pair 25
Chapter 5 Experiment 28
5.1 Simulation scenario 28
5.2 Impact of sensor density 30
5.3 Impact of size of field 31
5.4 Ideal scenario testing 32
5.5 Impact of error node density 33
5.6 Impact of node error variance 34
5.7 Impact of PU transmitter distance 35
5.8 Real device energy distribution correction 36
5.9 Analysis 44
Chapter 6 Conclusions and Future Works 45
References 46

參考文獻

[1] Apurva N. Mody, Gerald Chouinard, Greg Buchwald, and Winston Caldwell. IEEE 802.22 Working Group on Wireless Regional Area Networks. [Online]. http://www.ieee802.org/22/
[2] Yanyan Yang, Yunhuai Liu, Qian Zhang, and Lionel Ni, "Cooperative Boundary Detection for Spectrum Sensing Using Dedicated Wireless Sensor Networks," in INFOCOM, 2010.
[3] D. Cabric, A. Tkachenko, and R.W. Brodersen, "Spectrum Sensing Measurements of Pilot, Energy, and Collaborative Detection," in MILCOM, 2006.
[4] Yuan Yuan et al., "KNOWS: Cognitive Radio Networks Over White Spaces," in DySPAN, 2007.
[5] D. Datla, R. Rajbanshi, A.M. Wyglinski, and G.J. Minden, "Parametric Adaptive Spectrum Sensing Framework for Dynamic Spectrum Access Networks," in DySPAN, 2007.
[6] Amir Ghasemi and Elvino S. Sousa, "Optimization of Spectrum Sensing for Opportunistic Spectrum Access in Cognitive Radio Networks," in CCNC, 2007.
[7] Peng Qihang, Zeng Kun, Wang Jun, and Li Shaoqian, "A Distributed Spectrum Sensing Scheme Based on Credibility and Evidence Theory in Cognitive Radio Context ," in PIMRC, 2006.
[8] A.W. Min, Xinyu Zhang, and K.G. Shin, "Spatio-Temporal Fusion for Small-scale Primary Detection in Cognitive Radio Networks," in INFOCOM, 2010.
[9] Ziguo Zhong, Ting Zhu, Dan Wang, and Tian He, "Tracking with Unreliable Node Sequences," in INFOCOM, 2009.
[10] Yedavalli K. and Krishnamachari B., "Sequence-Based Localization in Wireless Sensor Networks," Mobile Computing, IEEE Transactions on , 2008.
[11] Wei Xi et al., "Locating sensors in the wild: pursuit of ranging quality," in SenSys, 2010.
[12] Kyouwoong Kim et al., "Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio," in DySPAN, 2007.
[13] Zhengwei Lu, Yi Ma, and R. Tafazolli, "A first-order cyclostationarity based energy detection approach for non-cooperative spectrum sensing ," in PIMRC, 2010.
[14] Hyoil Kim and K.G. Shin, "In-Band Spectrum Sensing in IEEE 802.22 WRANs for Incumbent Protection," Mobile Computing, IEEE Transactions on, 2010.
[15] E. Visotsky, S. Kuffner, and R. Peterson, "On collaborative detection of TV transmissions in support of dynamic spectrum sharing," in DySPAN.
[16] A. Ghasemi and E.S. Sousa, "Collaborative spectrum sensing for opportunistic access in fading environments ," in DySPAN, 2005.
[17] V. Aalo and R. Viswanathan, "Asymptotic performance of a distributed detection system in correlated Gaussian noise," Signal Processing, IEEE Transactions on , 1992.
[18] E. Peh and Ying-Chang Liang, "Optimization for Cooperative Sensing in Cognitive Radio Networks ," WCNC, 2007.
[19] Yi Zheng, Xianzhong Xie, and Lili Yang, "Cooperative Spectrum Sensing Based on SNR Comparison in Fusion Center for Cognitive Radio," in ICACC, 2009.
[20] Guanqun Yang, V. Shukla, and Daji Qiao, "A Novel On-Demand Framework for Collaborative Object Detection in Sensor Networks ," in INFOCOM, 2008.
[21] Sendora project. [Online]. http://www.sendora.eu/
[22] Viktoria Fodor and Ioannis Glaropoulos. (2009) Sensor networks for spectrum sensing-working. [Online]. http://www.ee.kth.se/commth/projects/CROPS/docs/C2.d1.pdf
[23] Yanyan Yang, Yunhuai Liu, Qian Zhang, and L. Ni, "Cooperative Boundary Detection for Spectrum Sensing Using Dedicated Wireless Sensor Networks ," in INFOCOM, 2010.
[24] Mark de Berg, Otfried Cheong, Marc van Kreveld, and Mark Overmars, Computational Geometry: Algorithms and Applications.: Springer-Verlag, 2008.
[25] Nello Cristianini and John Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods.: Cambridge University Press, 2000.
[26] Min Gao, Lan Cheng, Yunhuai Liu, and L. Ni, "SCAS: Sensing Channel ASsignment for Spectrum Sensing Using Dedicated Wireless Sensor Networks," in ICPADS, 2010.
[27] Z. Yang and Y. Liu, "Understanding Node Localizability of Wireless Ad-hoc Networks," Mobile Computing, IEEE Transactions on , 2011.
[28] Chongjoon You, Hongkyu Kwon, and Jun Heo, "Cooperative TV spectrum sensing in cognitive radio for Wi-Fi networks," Consumer Electronics, IEEE Transactions on , 2011.
[29] Lu Ye et al., "Modeling interference to DTV receivers from CR system in TV bands ," in WCNC, 2011.
[30] T. Yucek and H. Arslan, "A survey of spectrum sensing algorithms for cognitive radio applications," Communications Surveys & Tutorials, IEEE , 2009.

指導教授

張貴雲(Guey-Yun Chang)

審核日期

2011-7-28

推文