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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蘇炫榮(Hsuan-Jung Su) | |
dc.contributor.author | Alan Shenghan Tsai | en |
dc.contributor.author | 蔡昇翰 | zh_TW |
dc.date.accessioned | 2021-06-16T03:40:15Z | - |
dc.date.available | 2020-03-16 | |
dc.date.copyright | 2015-03-16 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-02-14 | |
dc.identifier.citation | Bibliography
[1] C.-H. Chang, H.-Y. Hsieh and H.-J. Su, “Not Every Bit Counts: Shifting the Focus from Machine to Data for Machine-to-Machine Communica- tions,” Asilomar Conference on Signals, Systems and Computers, Nov. 2012. [2] 3GPP Std. TR 37.868 v11.0.0, “Study on RAN Improvements for Machine-Type Communications,” Release 11, 2011. [3] Q. D. Vo, J.-P. Choi, H. M. Chang, and W. C. Lee, “Green Perspective Cognitive Radiobased M2M Communications for Smart Meters,” Proc. Int. Conf. Inf. Commun. Technol. Convergence, pp. 382-383, Nov. 2010. [4] Z. M. Fadlullah, M. M. Fouda, N. Kato, A. Takeuchi, N. Iwasaki, and Y. Nozaki, “Machine-to-Machine Communications for Healthcare,” Journal of Computing Science and Engineering, pp. 119-126, Jun. 2012. [5] Z. M. Fadlullah, M. M. Fouda, N. Kato, A. Takeuchi, N. Iwasaki, and Y. Nozaki, “Toward Intelligent Machine-to-Machine Communications in Smart Grid,” IEEE Commun. Mag., vol. 49, no. 4, pp. 60-65, Apr. 2011. [6] “Machine-to-Machine communications (M2M); Functional Architec- ture,” ETSI TS 102 690 V1.1.1, Oct. 2011. [7] D. Baron, M. F. Duarte, M. B. Wakin, S. Sarvotham, and R. G. Bara- niuk, “Distributed Compressive Sensing,” arXiv.org cs.IT, Jan. 2009. [8] W. U. Bajwa, K. Gedalyahu, and Y. C. Eldar, “Identification of Under- spread Linear Systems with Application to Super-Resolution Radar,” IEEE Transactions on Signal Processing, vol. 59, no. 6, pp. 2548-2561, Jun. 2011. [9] Z. Tian and G. B. Giannakis, “Compressed Sensing for Wideband Cog- nitive Radio,” n ICASSP, vol. 4, pp. 1357-1360, April. 2006. [10] E. Candes, J. K. Romberg, and T. Tao., “Stable Signal Recovery from Incomplete and Inaccurate Measurements,” Communications on Pure and Applied Mathematics, vol. 59, pp. 1207-1223, 2006. [11] E. J. Candes, “The Restricted Isometry Property and Its Implications for Compressed Sensing,” C. R. Acad. Sci. Paris, Ser. I, vol. 346, no. 12, pp. 589-592, Dec. 2008. [12] A. Cohen, W. Dahmen, and R. DeVore, “Compressed Sensing and Best k-term Approximation,” J. AMS, vol. 22, pp. 211-231, 2009. [13] M. Vehkapera, S. Chatterjee, and M. Skoglund, “Analysis of MMSE Estimation for Compressive Sensing of Block Sparse Signals,” Theory Workshop, pp. 553-557, Oct. 2011. [14] G. Yu and G Sapiro, “Statistical Compressed Sensing of Gaussian Mix- ture Models,” IEEE Trans of Signal Processing, vol. 59, no. 12, pp. 5842-5857, Dec. 2011. [15] C. R. Rao, “The Use and Interpretation of Principal Component Anal- ysis in Applied Research,” Sankhya: The Indian J. Statistics, vol. 26, pp. 329-358, 1964. [16] G. J. Pottie, W. J. Kaiser, “Wireless Integrated Network Sensors,” Com- munications of the ACM, vol. 43, no. 5, pp. 51-58, May, 2000. [17] Goldak, A. Chakravarti, M. Bibby, “A New Fnite Element Model for Welding Heat Source,” Metall. Trans. B, vol. 15B, vol. 21, pp. 299-305, Jan, 1984. [18] R.W. Ziolkowski, D.K. Lewis, B.D. Cook, “Evidence of Localized Wave Transmission,” Phys. Rev. Lett., vol. 62, pp. 147-150, Jan, 1989. [19] J. Berger, V. de Oliveira, and B. Sanso, “Objective Bayesian Analy- sis of Spatially Correlated Data,” Journal of the American Statistical Association, vol. 96, no. 456, pp. 1361-1374, 2001. [20] M. C. Vuran, O. B. Akan, and F. Akyildiz, “Spatio-Temporal Correla- tion: Theory and Applications for Wireless Sensor Networks,” Comput. Netw. J., vol. 45, no. 3, pp. 245-259, Jun. 21, 2004. [21] S. Sinanovic, S. N. Serafimovski, H. Haas, and G. Auer, “Maximizing the System Spectral Efficency in a Decentralised 2-link Wireless Network,” EURASIP Journal on Wireless Communications and Networking, 2008. [22] J. A. Gubner, “Probability and Random Processes for Electrical and Computer Engineers,” New York: Cambridge Univ. Press, 2006. [23] P. Frauenfelder, C. Schwab and R. A. Todor, “Finite Elements for El- liptic Problems with Stochastic Coeffcients,” Comput. Methods Appl. Mech. Engrg., pp. 205-228, 2005. [24] G. Quer, R. Masiero, G.Pillonetto, M. Rossi, and M. Zorzi, “Sensing, Compression, and Recovery for WSNs : Sparse Signal Modeling and Monitoring Framework,” IEEE Trans. on Wireless Comm., vol. 11, no. 10, pp. 3447-3461, Oct. 2012. [25] Ledoux M., “Concentration of Measure Phenomenon,” Providence, RI: Amer. Math. Soc., 2001. [26] V. V. Buldygin and Y. V. Kozachenko, “Metric Characterization of Ran- dom Variables and Random Processes,” Providence, RI: Amer. Math. Soc, 2000. [27] R. David, “Feedback shift register testing,” in Proc. FTCS 8, Tou- louse, France, pp. 103-107, June 1978. [28] R. Baraniuk, M. Davenport, R. DeVore, and M.Wakin, “A simple proof of the restricted isometry property for random matrices,” Construct. Approx., vol. 28, no. 3, pp. 253V263, 2008. [29] C. J. Willmott, K. Matsuura, “Advantages of the Mean Absolute Error (MAE) Over the Root Mean Square Error (RMSE) in Assessing Average Model Performance,” Climate Research, vol. 30, pp. 79-82, 2005. [30] R. Baraniuk, M. Davenport, M. Duarte, and C., “An Introduction to Compressive Sensing,” Addison-Wesley, 2011. [31] R. Calderbank, S. Howard, and S. Jafarpour, “Construction of a Large Class of Determinsitic Sensing Matrices That Satisfy a Statistical Isom- etry Property,” IEEE Trans. Inf. Theory, vol. 4, no. 2, pp. 358-374, Apr. 2010. [32] A. Averbuch, S. Dekel, and S. Deutsch, “Adaptive compressed image sensing using dictionaries,” SIAM J. Imag. Sci. [33] J. Haupt, R. Nowak, and R. M. Castro, “Adaptive sensing for sparse recovery,” in Proc. DSP/SPE, Marco Island, FL, 2009, pp. 702V707. [34] J. Haupt, R. M. Castro, and R. Nowak, “Distilled sensing: Selective sampling for sparse signal recovery,” IEEE Trans. Inf. Theory, vol. 57, no. 9, pp. 6222V6235, 2011. [35] A. Aldroubi, H. Wang, and K. Zarringhalam, “Sequential adaptive com- pressed sampling via Huffman codes,” Oct. 2008, arXiv:0810.4916v2. [36] A. Ashok, P. K. Baheti, and M. A. Neifeld, “Compressive imaging sys- tem design using task-specific information,” Appl. Opt., vol. 47, no. 25, pp. 4457V4471, 2008. [37] J. Ke, A. Ashok, and M. A. Neifeld, “Object reconstruction from adap- tive compressive measurements in feature-specific imaging,” Appl. Opt., vol. 49, no. 34, pp. H27VH39, 2010. [38] W. R. Carson, M. R. D. Rodrigues, M. Chen, L. Carin, and R. Calder- bank, “How to focus the discriminative power of a dictionary,” in Proc. Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 2012, to be pub- lished. [39] P. K. Baheti and M. A. Neifeld, “Recognition using information-op- timal adaptive feature-specific imaging,” J. Opt. Soc. Amer. A, vol. 26, no. 4, pp. 1055V1070, 2009. [40] R. Calderbank, L. Carin, and M. Chen, private communication, 2011. [41] S. Prasad, “Certain relations between mutual information and fidelity of statistical estimation,” Ann. Appl. Probab., vol. 17, no. 3, pp. 1102V1116, 2006. [42] T. M. Cover and J. A. Thomas, , D. L. Schilling, Ed., Elements of Information Theory, ser. Telecommunications, 2nd ed. New York: Wiley, 1991. [43] P. H. Schonemann, “A generalized solution of the orthogonal procrustes problem,” Psychometrika, vol. 31, no. 1, pp. 1V10, Mar. 1966. [44] Z. Nenadic, “Information discriminant analysis: Feature extraction with an information-theoretic objective,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 8, pp. 1394V1407, 2007. [45] M. Padmanabhan and S. Dharanipragada, “Maximizing information content in feature extraction,” IEEE Trans. Speech Audio Process., vol. 13, no. 4, pp. 512V519, 2005. [46] J. M. Duarte-Carvajalino, G. Yu, L. Carin, and G. Sapiro, “Task-driven adaptive statistical compressive sensing of gaussian mixture models,” IEEE Trans. Signal Processing, vol. 61, no. 3, pp. 585V600, 2013. [47] Steven M.Kay, “Fundamentals of statistical signal processing: estima- tion theory”,1993. [48] I. E. Telatar. “ Capacity of Multi-Antenna Gaussian Channels,Tech.” Rep. Bell Labs, Lucent Technologies, 1995. [49] J. M. Duarte-Carvajalino and G. Sapiro, “Learning to sense sparse sig- nals: Simultaneous sensing matrix and sparsifying dictionary optimiza- tion,” IEEE Trans. Image Process., vol. 18, no. 7, pp. 1395V1408, 2009. [50] K. Rosenblum, L. Zelnik-Manor, and Y. C. Eldar, “Sensing matrix op- timization for block-sparse decoding,” Sep. 2010. [51] D. Guo, S. Shamai (Shitz), and S. Verdu, “Mutual Information and Minimum Mean-Square Error in Gaussian Channels,” IEEE Trans. Inf. Theory, vol. 51, no. 4, pp. 1261 V 1283, Apr. 2005. [52] Y. Wu and S. Verdu, “Functional properties of MMSE and mutual infor- mation,” IEEE Trans. Inf. Theory, Vol. 58, no. 3,pp. 1289 - 1301,2011. [53] C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, pp. 379V423 and 623V656, Jul. and Oct. 1948. [54] M. Chen,C. Kuo, P.Lin,S. Lin and H. Su, “Low-Complexity Remote Compressive Sensing Schemes for Machine-to-Machine Networks with Stochastic Sources”Intelligent Green Building and Smart Grid (IG- BSG),pp.1 - 6, April 2014. [55] A. Lozano, A. Tulino, and S. Verdu, “Optimum power allocation for parallel Gaussian channels with arbitrary input distributions,” in IEEE Trans. Inf. Theory, July 2006, pp. 3033V3051. [56] F. Perez-Cruz, M. R. Rodrigues, and S. Verdu, “MIMO Gaussian chan- nels with arbitrary inputs: Optimal precoding and power allocation,” in IEEE Trans. Inf. Theory, Mar. 2010, pp. 1070V1084. [57] M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin, “Com- pressive sensing on manifolds using a nonparametric mixture of factor analyzers: Algorithm and performance bounds,” in IEEE Trans. Signal Process., vol. 58, no. 12, Dec. 2010, pp. 6140 V6155. [58] E. Candes and M. Wakin, “An introduction to compressive sampling,” in IEEE Signal Process. Mag., vol. 25, no. 2, 2008, pp. 21V30. [59] M. Payar’o and D. P. Palomar, “On optimal precoding in linear vector Gaussian channels with arbitrary input distribution”, in IEEE ISIT 09, July 2009, pp. 1085V1089. [60] M. Lamarca, “Linear precoding for mutual information maximization in MIMO systems”, in ISWCS 09, Sept. 2009, pp. 26V30. [61] Y. Wu and S. Verdu, “Functional properties of MMSE and mutual infor- mation,” IEEE Trans. Inf. Theory, Vol. 58, no. 3,pp. 1289 - 1301,2011. [62] C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, pp. 379V423 and 623V656, Jul. and Oct. 1948. [63] M. Chen, C. Kuo, P.Lin, S. Lin and H. Su, “Low-Complexity Remote Compressive Sensing Schemes for Machine-to-Machine Networks with Stochastic Sources”,Intelligent Green Building and Smart Grid (IG- BSG),pp.1 - 6, April 2014. [64] J. M. Cioffi and G. D. Forney, “Generalized Decision-Feedback Equaliza- tion for Packet Transmission with ISI and Gaussian Noise”, in ”Com- munications, Computation, Control and Signal Processing,A. Paulraj, V. Roychowdhury and C. Schaper, Eds, Kluwer, 1997. [65] J. Yang and S. Roy, “Joint Transmitter-Receiver Optimization for Multi- Input Multi-Output Systems with Decision Feedback”, IEEE Trans. In- format. Theory, vol. 40, pp. 1334V1347, Sept. 1994. [66] G. Quer, D. Zordan, R. Masiero, M. Zorzi, and M. Rossi, “WSN-Control: Signal Reconstruction through Compressive Sensing in Wireless Sensor Networks,” in IEEE SenseApp Workshop, Denver, CO, US, Dec. 2010, pp. 937V944. [67] D. Zordan, G. Quer, M. Zorzi, and M. Rossi, “Modeling and generation of space-time correlated signals for sensor network fields,” in Proc. IEEE GLOBECOM, Houston, TX, USA, Dec. 2011, pp. 1V6. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54867 | - |
dc.description.abstract | 在最近幾年,互聯網 (M2M) 被廣泛應用於無限通訊系統中。機器本身會有功率限制,
處理和通訊能力也都會有限。壓縮感測技術可以避免傳多餘的資訊出去,進而壓縮傳送資料 量。在此篇論文中,在雙層架構下,我們於互聯網中針對隨機訊號源提出一個遠程的壓縮感 知架構,目的是壓縮閘道 (gateway) 的傳輸資料,並且我們將原本問題簡化成隨機壓縮感知 的問題。進一步,針對原本的系統中,在壓縮閘道到基地台中間,考慮通過一個白色雜訊的 通道的情況,讓系統更貼近真實生活的環境。 最後,我們找到兩種產生感知矩陣的方法。一個是奇異值分解共變異數矩陣 ( SVD Covariance Matrix),這方法是使用機器跟機器之間在空間上存在的關聯性來壓縮傳送資料。 另一個是適應性統計壓縮感知(Adaptive Statistical Compressive Sensing),這方法是參考過去傳 送過的資料,結合互信息來產生新的感知矩陣。將兩種方法應用於我們所提出的系統中,並 且驗證我們所選擇的這兩種感知矩陣在不同的系統下,可以達到傳統訊號傳輸所能達到的效 果優於之前的高斯 (Gaussian)或伯努力(Bernoulli) 感知矩陣。 | zh_TW |
dc.description.abstract | In recent years, machine-to-machine (M2M) networks are widely con-
sidered in wireless communication systems. To avoid the transmission of redundant information to improve the data rate, compressive sensing is a promising tool to be considered. In this paper under the two-tier architec- ture, we propose a remote compressive sensing scheme for the M2M networks with stochastic sources to improve the data rate and formulate a statistical compressive sensing problem. First we propose to use the minimum mean square error estimator at the gateway and the base station to transform the problem as a noisy statistical CS. We derive the form of a optimal decoder by following MMSE estimation for the proposed scheme. Furthermore We find two ways, that can produce the sensing matrices. There are SVD covariance matrix(SCM) and adaptive statistical compressive sensing(ASCS). SCM is using machines covariance matrix to compressed the data by reducing the correlation between each machines. ASCS uses the previous measurements and the sensing matrices obtained in the past states and combines and com- bine the mutual information to become a new method of CS. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T03:40:15Z (GMT). No. of bitstreams: 1 ntu-104-R01942138-1.pdf: 606002 bytes, checksum: da8ef05ad7477816003e0d2d76eb551e (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | Contents
1 Introduction 1 1.1 Machine-to-Machine Networks . . . . . . . . . . . . . . . . . . 1 1.2 Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Statistical Compressed Sensing of Gaussian Source . . . . . . 4 1.4 Why Use Gaussian Source . . . . . . . . . . . . . . . . . . . . 5 1.5 Adaptive Statistical Compressive Sensing . . . . . . . . . . . . 5 1.6 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.7 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 System Model 8 2.1 Proposed Remote Compressive Sensing Schemes for Machine- to-Machine Networks over noisy channel . . . . . . . . . . . . 9 2.2 Adaptive Remote Compressive Sensing Schemes for M2M Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3 Statistical Compressive Sensing for Gaussian Signals 15 3.1 Previous Research Results . . . . . . . . . . . . . . . . . . . . 15 3.1.1 The Restrictive Isometry Property (RIP) . . . . . . . . 15 3.1.2 Random Sensing Matrices that satisfies the RIP . . . . 19 3.2 The Optimal Decoder for Gaussian Signals . . . . . . . . . . . 21 3.3 Design Sensing Matrix . . . . . . . . . . . . . . . . . . . . . . 25 3.3.1 Mean Square Error and Mutual Information in Gaus- sian Channels . . . . . . . . . . . . . . . . . . . . . . . 25 3.3.2 SVD Covariance Sensing Matrix . . . . . . . . . . . . . 28 3.4 Adaptive Statistical Compressive Sensing . . . . . . . . . . . . 31 4 Simulation Results 35 4.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . 35 4.2 Performance Comparison . . . . . . . . . . . . . . . . . . . . . 36 4.2.1 The overall performance . . . . . . . . . . . . . . . . . 37 4.2.2 Remote Compressive Sensing Over Noisy Channel . . . 38 4.2.3 Remote Compressive Sensing with SVD Covariance Ma- trix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2.4 Adaptive Remote Compressive Sensing . . . . . . . . . 42 5 Conclusions 43 6 Future Works 45 Bibliography 46 | |
dc.language.iso | en | |
dc.title | 使用遠程壓縮感測技術在有雜訊的物聯網 | zh_TW |
dc.title | Remote Compressive Sensing for Noisy Machine-to-Machine Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 蘇柏青,林秉勳 | |
dc.subject.keyword | 互聯網,毛細管網路,壓縮感知,奇異值分解,共變異數矩陣,適應性統計壓縮感知, | zh_TW |
dc.subject.keyword | Machine-to-Machine networks,Compressive sensing,Noisy channel,Mutual information,SVD covariance matrix,Adaptive statistical compressive sensing, | en |
dc.relation.page | 55 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-02-14 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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