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研究生:李庭萱
研究生(外文):Lee, Ting-Hsuan
論文名稱:流動介質中的粒子通訊:在傳輸端有平均及最大延遲的限制下,相加性反高斯雜訊通道容量界線
論文名稱(外文):Molecular Communication in a Liquid: Bounds on the Capacity of the Additive Inverse Gaussian Noise Channel with Average and Peak Delay Constraints
指導教授:莫詩台方
指導教授(外文):Moser, Stefan M.
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:46
中文關鍵詞:反高斯粒子傳輸
外文關鍵詞:Inverse GaussianMolecular Communication
相關次數:
  • 被引用被引用:0
  • 點閱點閱:146
  • 評分評分:
  • 下載下載:8
  • 收藏至我的研究室書目清單書目收藏:0
在本篇論文中,我們探討一個相當新的通道模型,此通道是藉由原子在液體中的交換來傳輸訊號。而我們假設原子在傳輸過程中,是在一維的空間做移動。像是我們將奈米級的儀器放入血管中,而此儀器在人體內和其他儀器交換訊息就是一個很典型的通訊應用。一旦原子被釋放在液體中,將會在液體中進行布朗運動,進而造成我們無法預估原子到達傳輸端的時間,換句話說,布朗運動造成接收時間的不確定性,而此不確定性就是我們的雜訊,而我們用反高斯分布來描述此雜訊。此篇研究重點是相加性雜訊通道,在有平均以及最大延遲的限制下,基本的通道容量趨勢。
我們深入研究此模型,並且分析出新的通道容量的上界與下界,而這些界線是逐漸靠近的,也就是說,如果我們允許平均以及最大延遲放寬到無限到,亦或是介質的流體速度趨近無限大,我們可以得到準確的通道容量。
In this thesis a very recent and new channel model is investigated that describes communication based on the exchange of chemical molecules in a liquid medium with constant drift. The molecules travel from the transmitter to the receiver at two ends of a one-dimensional axis. A typical application of such communication are nano-devices inside a blood vessel communicating with each other. In this case, we no longer transmit our signal via electromagnetic waves, but we encode our information into the emission time of the molecules. Once a molecule is emitted in the fluid medium, it will be affected by Brownian motion, which causes uncertainty of the molecule’s arrival time at the receiver. We characterize this noise with an inverse Gaussian distribution. Here we focus solely on an additive noise channel to describe the fundamental channel capacity behavior with average and peak delay constraints.
This new model is investigated and new analytical upper and lower bounds on the capacity are presented. The bounds are asymptotically tight, i.e., if the average-delay and peak-delay constraints are loosened to infinity, the corresponding asymptotic capacities are derived precisely.
Contents
1 Introduction 1
1.1 General Molecular Communication Channel Model . . . . . . . . . . 1
1.2 MathematicalModel........................... 2
1.3 Capacity.................................. 4
2 Mathematical Preliminaries 7
2.1 Properties of the Inverse Gaussian Distribution . . . . . . . . . . . . 7 2.2 RelatedLemmasandPropositions ................... 10
3 Known Bounds to the Capacity of the AIGN Channel with Only
an Average Delay Constraint 13
4 Main Result 17
5 Derivations 25
5.1 ProofofUpper-BoundofCapacity ................... 25 5.1.1 For0<α<0.5.......................... 25 5.1.2 For0.5≤α≤1.......................... 29
5.2 ProofofLower-BoundofCapacity ................... 30 5.2.1 For0<α<0.5.......................... 30 5.2.2 For0.5≤α≤1.......................... 34
5.3 AsymptoticCapacityofAIGNChannel ................ 35 5.3.1 WhenTLarge........................... 36 5.3.2 vLarge .............................. 37
6 Discussion and Conclusion 41
List of Figures 43
Bibliography 45

Bibliography
[1] K. V. Srinivas, R. S. Adve, and A. W. Eckford, “Molecular communication in fluid media: The additive inverse Gaussian noise channel,” December 2010, arXiv:1012.0081v2 [cs.IT]. [Online]. Available: http://arxiv.org/abs/1012.0081 v2
[2] C. E. Shannon, “A mathematical theory of communication,” Bell System Tech- nical Journal, vol. 27, pp. 379–423 and 623–656, July and October 1948.
[3] H.-T. Chang and S. M. Moser, “Bounds on the capacity of the additive inverse Gaussian noise channel,” in Proceedings IEEE International Symposium on In- formation Theory (ISIT), Cambridge, MA, USA, July 1–6, 2012, pp. 304–308.
[4] R. S. Chhikara and J. L. Folks, The Inverse Gaussian Distribution — Theory, Methodology, and Applications. New York: Marcel Dekker, Inc., 1989.
[5] I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products, 6th ed., A. Jeffrey, Ed. San Diego: Academic Press, 2000.
[6] V. Seshadri, The Inverse Gaussian Distribution — A Case Study in Exponential Families. Oxford: Clarendon Press, 1993.
[7] T. Kawamura and K. Iwase, “Characterizations of the distributions of power inverse Gaussian and others based on the entropy maximization principle,” Journal of the Japan Statistical Society, vol. 33, no. 1, pp. 95–104, January 2003.
[8] T. M. Cover and J. A. Thomas, Elements of Information Theory, 2nd ed. New York: John Wiley &; Sons, 2006.
[9] A. Lapidoth and S. M. Moser, “Capacity bounds via duality with applications to multi-antenna systems on flat fading channels,” June 25, 2002, submitted to IEEE Transactions on Information Theory, available at <http://www.isi.ee.ethz.ch/∼moser>. [Online]. Available: http://www.isi .ee.ethz.ch/∼moser
[10] S. M. Moser, Information Theory (Lecture Notes), version 1, fall semester 2011/2012, Information Theory Lab, Department of Electrical Engineering,
45
Bibliography
National Chiao Tung University (NCTU), September 2011. [Online]. Available: http://moser.cm.nctu.edu.tw/scripts.html
[11] W. Schwarz, “On the convolution of inverse Gaussian and exponential ran- dom variables,” Communications in Statistics — Theory and Methods, vol. 31, no. 12, pp. 2113–2121, 2002.
[12] M. Abramowitz and I. Stegun, Handbook of Mathematical Functions With For- mulas, Graphs, and Mathematical Tables, 10th ed. New York: Dover Publica- tions, 1965.
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