以因子圖為基礎之感知無線電系統稀疏頻譜偵測

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：68

、訪客IP：3.135.217.212

姓名

劉泰隆(Tai-lung Liu) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

以因子圖為基礎之感知無線電系統稀疏頻譜偵測
(Factor Graph-Based Sparse Spectrum Sensing for Cognitive Radio Systems)

相關論文

★ 基於干擾對齊方法於多用戶多天線下之聯合預編碼器及解碼器設計	★ 應用壓縮感測技術於正交分頻多工系統之稀疏多路徑通道追蹤與通道估計方法
★ 應用於行動LTE 上鏈SC-FDMA 系統之通道等化與資源分配演算法	★ Sparse Spectrum Detection with Sub-blocks Partition for Cognitive Radio Systems
★ 中繼網路於多路徑通道環境下基於領航信號的通道估測方法研究	★ 基於代價賽局在裝置對裝置間通訊下之資源分配與使用者劃分
★ 應用於多用戶雙向中繼網路之聯合預編碼器及訊號對齊與天線選擇研究	★ 多用戶波束成型和機會式排程於透明階層式蜂巢式系統
★ 應用於能量採集中繼網路之最佳傳輸策略研究設計及模擬	★ 感知無線電中繼網路下使用能量採集的傳輸策略之設計與模擬
★ 以綠能為觀點的感知無線電下最佳傳輸策略的設計與模擬	★ 二使用者於能量採集網路架構之合作式傳輸策略設計及模擬
★ 基於Q-Learning之雙向能量採集通訊傳輸方法設計與模擬	★ 多輸入多輸出下同時訊息及能量傳輸系統之設計與模擬
★ 附無線充電裝置間通訊於蜂巢式系統之設計與模擬	★ 波束成形與資源分配於無線充電蜂巢式網路之設計與模擬

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]

至系統瀏覽論文 ( 永不開放)

摘要(中)

隨著無線通訊服務快速蓬勃發展，資料傳輸量與頻寬需求量持續倍增，現有靜態頻譜分配方式已無法負荷未來通訊應用服務發展。感知無線電(Cognitive radio, CR)為一新興技術，藉由即時偵測未被充分利用之空頻帶，提供給次用戶(Second user, SU)更多動態存取頻譜(Dynamic Spectrum Access, DSA)的機會，有效改善頻寬利用效率。然而在滿足奈奎斯取樣定理(Nyquist sampling theorem)之下，感知無線電對於偵測稀疏頻帶(Sparse spectrum)之效率不高外，偵測複雜度將隨著所偵測的頻寬變大而增加；再者無線通道產生的遮蔽效應(Shadow fading)也將大幅降低頻譜偵測準確性。
於本論文中，我們設計一寬頻感知正交分頻多工系統(Orthogonal frequency division multiplexing, OFDM)，藉由壓縮感測(Compressive sensing, CS)技術來減少偵測所需取樣點與改善頻譜偵測效能。在貝氏架構(Bayesian framework)下，將問題透過因子圖(Facto-Graph, FG)來解析，並經由可信傳遞(Belief propagation, BP)演算法以疊代方式趨近於全域最佳解。藉由信號之稀疏特性，我們提出子區塊分割法(Sub-block partition method )來簡化信息傳遞法(Message passing algorithm)在數學上之積分問題。
此外，由於訊號具有空間分集特性(Spatial diversity)，因此次用戶基地台可利用分散式合作通訊(Distributed cooperative communication)將偵測主用戶(Primary user, PB)頻帶使用結果，同樣經由信息傳遞與其他次用戶基地台(Secondary base station)間彼此交換訊息，以多次遞迴方式來判定空洞頻帶，有效改善感知無線電於嚴重遮蔽效應下之頻譜偵測效能。

摘要(英)

With the growth in wireless services, the demand for high data rate wireless multimedia applications has been significantly increasing recently. The current static spectrum allocation policy is inefficient, and the spectrum utilization of some outdated licensed bands is even more spare.
　　Cognitive Radio (CR) is emerging technology to improve the efficiency of spectrum utilization by allowing other unlicensed users to utilize unoccupied spectrum holes through Dynamic Spectrum Access(DSA). However, according to the Nyquist sampling theorem, to sensing the sparse spectrum is inefficient, and the computational complexity and cost will also rise in wideband spectrum sensing. Moreover, in urban areas, it is a major challenge to reliably sense the vacant spectrum due to the hidden terminal problem and severe shadow fading.
In this thesis, we design an orthogonal frequency division multiplexing(OFDM) system for compressive spectrum sensing that can sample sparse signal under sub-Nyquist rate to ease the complexity. Under the Bayesian inference framework, we apply an efficient algorithm belief propagation(BP) that is proposed to solve the Bayesian maximum-a-posterior(MAP) inference problem by sequentially and iteratively passing messages. Since the signal has sparse property, we proposed a sub-block partition method to simplify the mathematically integral problem in message passing algorithm。
　　Furthermore, in our system model, multiple secondary base-stations(BSs) can mitigate the uncertainty of single detection by distributed cooperative spectrum sensing due to spatial diversity. All base stations can communicate through the backhaul by passing message iteratively to overcome the severe shadowing and improving the spectrum sensing accuracy.

關鍵字(中)

★ 壓縮感測
★ 因子圖
★ 可信傳遞演算法
★ 子區塊分割法
★ 信息傳遞法

關鍵字(英)

★ Compressive sensing
★ Facto-Graph
★ Belief propagation
★ Sub-block partition method
★ Message passing algorithm

論文目次

致謝 II
摘要 III
ABSTRACT IV
目錄 V
圖目錄 VII
表目錄 IX
符號說明 X
第一章緒論 1
1.1 研究背景 1
1.2 文獻探討 2
第二章研究理論介紹 4
2.1 感知無線電(Cognitive Radio, CR) 4
2.2 壓縮感測技術(Compressive Sensing, CS) 5
2.3 有限等距限制(Restricted Isometry Property, RIP) 5
2.4 可信傳遞演算法 (Belief propagation, BP) 6
2.4.1 因子圖(Factor graph, FG)之介紹 7
2.4.2 總和乘積(Sum-Product)與最大乘積(Max-Product)演算法 8
2.5 合作式通訊(Cooperative communication) 11
2.6 能量偵測(Energy detection) 12
第三章次用戶系統貝式壓縮感測技術 14
3.1 單一次用戶系統之壓縮感測系統模型 14
3.2 單一次用戶系統之貝式機率推論 16
3.2.1 貝式機率推論 16
3.2.2 機率函數定義 18
3.3 多重次用戶系統之壓縮感測系統模型 21
3.4 多重次用戶系統之貝式機率推論 22
第四章貝式壓縮感測技術之因子圖推論 24
4.1 單一次用戶系統之因子圖推論 24
4.2 因子圖之訊息傳遞演算法 26
4.3 單一次用戶系統之信息傳遞排程(Message passing scheduling) 33
4.4 多重次用戶系統之因子圖推論 35
4.5 多重次用戶系統之信息傳遞排程 36
第五章模擬結果與討論 38
5.1 評估效能之檢測方法 38
5.2 模擬與討論 40
5.2.1比較信號接收時間與空乏頻帶感知效能 41
5.2.2比較信號接收時間與可使用子載波感知效能 42
5.2.3比較信號壓縮百分比與空乏頻帶感知效能 44
5.2.4調整信號壓縮比及基地台數之空乏頻帶感知效能與能量偵測比較 47
5.2.5比較子區塊分割數目與空乏頻帶感知效能 50
第六章結論 53
中英對照表 54
參考文獻(Reference) 57

參考文獻

[1] G. Staple and K. Werbach, “The end of spectrum scarcity,” in Proc. IEEE Spectrum Archive, vol. 41, no. 3, pp. 48–52, March 2004.
[2] S. Haykin, “Cognitive Radio: Brain-empowered wireless communications,” in Proc. IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220,February 2005.
[3] H. Kim and K. G. Shin, “Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks,” in Proc. IEEE Trans. Mobile Comput., vol. 7, no. 5, pp. 533–545, May 2008.
[4] M.-L. Ku, (no.36 Feb. 2012),「寬頻感知無線電及壓縮感知技術簡介」,Proc. Networked Commun. Program(NCP), Newslett., Taiwan., [Online]. Available: http://www.ce.ncu.edu.tw/~mlku/NCP_mlku.pdf
[5] H. T. Yu, "Lifetime Maximization of Secondary Cooperative Systems in Underlay Cognitive Radio Networks", Dept. of Inst. of Commun., Nat. Sun Yat-sen Univ. [Online]. Available: http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dnclcdr&s=id=%22100NSYS5650103%22.&searchmode=basic
[6] J. Meng, W. Yin, H. Li; E. Hossain and Z. Han, "Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks," in Proc. IEEE J. Sel. Areas Commun. , vol.29, no.2, pp.327-337, Feb. 2011.
[7] D. Mesecher, L. Carin, I. Kadar and R. Pirich, “Exploiting Signal Sparseness for Reduced-rate Sampling,” in Proc. IEEE Systems App. and Techno. Conf., pp.1-6, May 2009.
[8] C. J. Emamnuel, “Compressive Sampling,” in Proc. Int. Congress of Math., 2006.
[9] Shihao Ji, Ya Xue and Lawrence Carin, “Baysian Compressive Sensing,” in Proc. IEEE Trans. Signal Process., vol. 56, pp. 2346-2356, June 2008.
[10] R. G. Baraniuk, "Compressive Sensing [Lecture Notes]," in Proc. IEEE Signal Processing Mag., vol.24, no.4, pp.118-121, July 2007.
[11] J. Liang, Y. Liu, W. Zhang, Y. Xu, X. Gan and X. Wang, “ Joint Compressive Sensing in Wideband Cognitive Networks” in Proc. 2009 IEEE Wireless Commun. and Networking Conf.(WCNC), pp.1-5, April 2010.
[12] R. Masiero, G. Quer, M. Rossi and M. Zorzi, “A Bayesian Analysis of Compressive Sensing Data Recovery in Wireless Sensor Networks,” in Proc. 2009 Ultra Modern Telecommun. & Workshops(ICUMT) Int.Conf. pp.1-6, Oct. 2009.
[13] D. Baron, S. Sarvotham, and R. G. Baraniuk, "Bayesian Compressive Sensing Via Belief Propagation," in Proc. IEEE Trans. Signal Process., on , vol.58, no.1, pp.269-280, Jan. 2010.
[14] S. D Babacan, R. Molina, and A. K. Katsaggelos, "Bayesian Compressive Sensing Using Laplace Priors," in Proc. IEEE Trans. Image Process., vol.19, no.1, pp.53-63, Jan. 2010
[15] H.-A Loeliger, J. Dauwels, J. Hu, S. Korl, Li Ping, and F. R. Kschischang, "The Factor Graph Approach to Model-Based Signal Processing," Proc. of the IEEE , vol.95, no.6, pp.1295-1322, June 2007
[16] L. Lei, R. Yates, P. Spasojevic and L. Greenstein, "Cooperative sensing of primary users in cognitive radio networks based on message passing," in Proc. Inform. Sci. and Syst.,(CISS) 43rd Annu, pp.568-573, March 2009
[17] J. Ziniel and P. Schniter,, "Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem," in Proc. IEEE Trans. Signal Process., vol.61, no.2, pp.340-354, Jan.15, 2013
[18] P. Schniter, "Turbo reconstruction of structured sparse signals," in Proc. Inform. Sci. and Syst.,(CISS) 44rd Annu,, pp.1,6, 17-19 March 2010
[19] Z. Fanzi, C. Li and Z. Tian, “Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks,” in Proc. IEEE J. Sel. Topics Signal Process., Vol.5, no.1, pp.1-12, June 2010.
[20] M.-L Ku, Q. Chen, S.S. Ghassemzadeh, V. Tarokh, and L.-C Wang, "Service coverage for cognitive radio networks with cooperative relays in shadowed hotspot areas," in Proc. 2011 IEEE Wireless Commun. and Networking Conf.(WCNC), pp.1759-1764, March 2011.
[21] J.J. Popoola and R. V. Olst, "A Novel Modulation-Sensing Method," in Proc. IEEE Veh. Technol. Mag. vol.6, no.3, pp.60-69, Sept. 2011.
[22] D. Willkomm, S. Machiraju, J. Bolot, and A. Wolisz, "Primary user behavior in cellular networks and implications for dynamic spectrum access," in Proc. IEEE Commun.Conf., vol.47, no.3, pp.88-95, March 2009
[23] H. Mutlu, M. Alanyali and D. Starobinski, "Spot Pricing of Secondary Spectrum Usage in Wireless Cellular Networks," in Proc. IEEE Comput. Commun. Conf(INFOCOM), 27th. pp.682-690, April 2008.
[24] H. Mutlu, M. Alanyali, and D. Starobinski, "Secondary Pricing of Spectrum in Cellular CDMA Networks," in Proc. IEEE Int Symp., New Frontiers in Dynamic Spectrum Access Networks( DySPAN),2nd, pp.535-542, April 2007
[25] X. Zhou, Y. Li, H.-K Young, and A. C. K. Soong, "Detection Timing and Channel Selection for Periodic Spectrum Sensing in Cognitive Radio," Proc. IEEE GLOBECOM , pp.1-5, Nov. 2008.
[26] J. Ma; Y. Li, "A Probability-Based Spectrum Sensing Scheme for Cognitive Radio," Proc. IEEE Int. Commun. Conf.(ICC), pp.3416-3420, May 2008
[27] G. Zhang, J. Liu, L. Chen and W. Guo, "Efficient energy detector for spectrum sensing in complex Gaussian noise," Proc. Wireless Commun. and Signal Process. (WCSP), Int. Conf. , pp.1-4, Oct. 2010
[28] B. H. Juang, G. Y. Li, and M. Jun, “Signal processing in cognitive radio,” Proc. IEEE, vol. 97,pp. 805-823, May2009.
[29] R. Tandra and A. Sahai, “SNR walls for signal detection,” Proc. IEEE J. Sel.Topics Signal Process., vol.2, no. 1, pp. 4-17, Feb. 2008.

指導教授

古孟霖(Meng-lin Ku)

審核日期

2013-8-27

推文