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研究生:丹竹文
研究生(外文):Juwendo Denis
論文名稱:Robust Adaptive Distributed Beamforming Schemes for Time-Varying Fading Channels: Performance Analysis and Algorithm Design
論文名稱(外文):時變性通道下穩健性適應性分散式波束成型之效能分析以及演算法設計
指導教授:林澤林澤引用關係
指導教授(外文):Lin, Che
學位類別:碩士
校院名稱:國立清華大學
系所名稱:通訊工程研究所
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:41
中文關鍵詞:分散式波束成型
外文關鍵詞:Distributed Beamforming
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Considerable works on adaptive schemes for transmit beamforming in distributed networks have emerged in the past years. In all these works, it was assumed that channels between all transmitters and the re- ceiver experience frequency flat slow-fading and a static environment was often considered. In practical environments, however, system uncertainties such as channels fluctuations, networks random node ad- dition and random node removal may rise and the aforementioned ideal assumptions may fail in these settings. Therefore, we focus on robust designs in this thesis and proposed a systematic analytical framework where stochastic stability is employed to demonstrate the tracking capability of the general adaptive schemes when channels are subject to fast variations in time. In addition, for time-varying channel and time-varying network topology, we defined a set of robustness criteria that can be used as comparison metrics for existing adaptive schemes. By utilizing the proposed analytical frameworks and metrics, we develop an bio-inspired scheme, BioRARSA2, that possess significantly superior ro- bustness with respect to environmental variations and system uncertainties. The improved robustness of the proposed algorithm is further validated through extensive numerical simulations.
Considerable works on adaptive schemes for transmit beamforming in distributed networks have emerged in the past years. In all these works, it was assumed that channels between all transmitters and the re- ceiver experience frequency flat slow-fading and a static environment was often considered. In practical environments, however, system uncertainties such as channels fluctuations, networks random node ad- dition and random node removal may rise and the aforementioned ideal assumptions may fail in these settings. Therefore, we focus on robust designs in this thesis and proposed a systematic analytical framework where stochastic stability is employed to demonstrate the tracking capability of the general adaptive schemes when channels are subject to fast variations in time. In addition, for time-varying channel and time-varying network topology, we defined a set of robustness criteria that can be used as comparison metrics for existing adaptive schemes. By utilizing the proposed analytical frameworks and metrics, we develop an bio-inspired scheme, BioRARSA2, that possess significantly superior ro- bustness with respect to environmental variations and system uncertainties. The improved robustness of the proposed algorithm is further validated through extensive numerical simulations.
1 Introduction 1
2 Problem Formulation 5
2.1 Distributed Beamforming with Limited Feedback .................... 6
2.2 Adaptive Distributed Beamforming with Time-Varying Channels and Network Topology 8
3 Analytical Frameworks and Robustness Criteria 10
3.1 Random Search in Static Environments ......................... 10 3.2 Random Search with Time-Varing Objective Function. . . . . . . . . . . . . . . . . . 12
4 Proposed Algorithm 19
4.1 Algorithm Description .................................. 21 4.2 Convergence for Noiseless Measurement......................... 24
4.3 Important Characteristic of BioRARSA2......................... 25
5 Simulation result 27
5.1 Noise-free static environments .............................. 28
5.2 Noise-freeTime-varying Environments ......................... 30
6 Conclusion Appendix
34
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[2] R. Mudumbai, G. Barriac, and U. Madhow, “On the feasibility of distributed beamforming in wireless networks,” IEEE Transactions on Wireless Communications, vol. 6, no. 5, pp. 1754 – 1763, may 2007.
[3] R. Mudumbai, B. Wild, U. Madhow, and K. Ramch, “Distributed beamforming using 1 bit feed- back: From concept to realization,” in Allerton Conference on Communication, Control, and Computing, 2006.
[4] I. Thibault, G. Corazza, and L. Deambrogio, “Random, deterministic, and hybrid algorithms for distributed beamforming,” in Advanced satellite multimedia systems conference and the 11th sig- nal processing for space communications workshop, 2010.
[5] C.-S.Tseng,C.-C.Chen,andC.Lin,“Abio-inspiredrobustadaptiverandomsearchalgorithmfor distributed beamforming,” in IEEE International Conference on Communications, June 2011.
[6] S. Song and J. Thompson, “One-bit feedback algorithm with decreasing step size for distributed beamforming,” in 2010 Second UK-India-IDRC International Workshop on Cognitive Wireless Systems (UKIWCWS), Dec. 2010.
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[7] P.Fertl,A.Hottinen,andG.Matz,“Perturbation-baseddistributedbeamformingforwirelessrelay networks,” in IEEE Global Telecommunications Conference, 2008.
[8] ——, “A multiplicative weight perturbation scheme for distributed beamforming in wireless relay networks with 1-bit feedback,” in IEEE International Conference on Acoustics, Speech and Signal Processing, 2009.
[9] I. Thibault, G. Corazza, and L. Deambrogio, “Phase synchronization algorithms for distributed beamforming with time varying channels in wireless sensor networks,” in 7th International Wire- less Communications and Mobile Computing Conference (IWCMC), Jul. 2011.
[10] J. Denis, C.-S. Tseng, C.-W. Lee, C.-Y. Tsai, and C. Lin, “Deterministic bisection search algo- rithm for distributed sensor/relay networks,” in Global Communications Conference (GLOBE- COM), 2012 IEEE, 2012, pp. 4851–4855.
[11] J. Bucklew and W. Sethares, “Convergence of a class of decentralized beamforming algorithms,” IEEE Transactions on Signal Processing, vol. 56, 2008.
[12] C. Lin, V. Veeravalli, and S. Meyn, “A random search framework for convergence analysis of dis- tributed beamforming with feedback,” IEEE Transactions on Information Theory, vol. 56, no. 12, pp. 6133 –6141, dec. 2010.
[13] C.-C. Chen and C. Lin, “Adaptive distributed beamforming for relay networks: Convergence analysis,” submitted to IEEE Transactions on Signal Processing, 2011.
[14] S. Song, J. Thompson, P.-J. Chung, and P. Grant, “Exploiting negative feedback information for one-bit feedback beamforming algorithm,” IEEE Transactions on Wireless Communications, vol. 11, no. 2, pp. 516 –525, Feb. 2012.
[15] H. J. Kushner, Stochastic Stability and Control. Academy Press Inc., 1967.
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[16] H. Kushner, “On the construction of stochastic liapunov functions,” Automatic Control, IEEE Transactions on, vol. 10, no. 4, pp. 477–478, 1965.
[17] L. Weiss and E. F. Infante, “On the stability of systems defined over a finite time interval,” Proceedings of the National Academy of Sciences, vol. 54, no. 1, pp. 44–48, July 1965. [Online]. Available: http://www.pnas.org/content/54/1/44.short
[18] K. Passino, “Biomimicry of bacterial foraging for distributed optimization andcontrol,” IEEE Control Systems Magazine, vol. 22, 2002.
[19] H. Kushner, “Finite time stochastic stability and the analysis of tracking systems,” Automatic Control, IEEE Transactions on, vol. 11, no. 2, pp. 219–227, 1966.
[20] H. J. Kushner, Introduction to Stochastic Control. Holt, Rinehart and Winston, Inc., 1971.
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