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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電信工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68437
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor蘇炫榮
dc.contributor.authorTsz-Ying Chungen
dc.contributor.author鐘梓瀅zh_TW
dc.date.accessioned2021-06-17T02:21:02Z-
dc.date.available2019-08-22
dc.date.copyright2017-08-28
dc.date.issued2017
dc.date.submitted2017-08-20
dc.identifier.citation[1] A. Alkhateeb, O. El Ayach, G. Leus, and R. Heath, “Channel estimation and hybrid precoding for millimeter wave cellular systems,” IEEE J. Sel. Topics Signal. Process., vol. 8, no. 5, pp. 831–846, Oct. 2014.
[2] F. Sohrabi and W. Yu, “Hybrid digital and analog beamforming design for large-scale MIMO systems,” Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Brisbane, Australia, Apr. 2015, pp. 2929– 2933.
[3] R. Méndez-Rial, C. Rusu, N. González-Prelcic, A. Alkhateeb, and R. W. Heath, Jr. “Hybrid MIMO architectures for millimeter wave communications: Phase shifters or switches?” IEEE Access, vol. 4, pp. 247–267, Jan. 2016.
[4] R. W. Heath, Jr., N. González-Prelcic, S. Rangan, W. Roh, and A. M. Sayeed, “An overview of signal processing techniques for millimeter wave MIMO systems,” IEEE J. Sel. Topics Signal Process., vol. 10, no. 3, pp. 436–453, Apr. 2016.
[5] A. Alkhateeb, G. Leus, and R. W. Heath, Jr., “Limited feedback hybrid precoding for multi-user millimeter wave systems,” IEEE Trans. Wireless Commun., vol. 14, no. 11, pp. 6481–6494, Nov. 2015.
[6] O. El Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, Jr., “Spatially sparse precoding in millimeter wave MIMO systems,” IEEE Trans. Wireless Commun., vol. 13, no. 3, pp. 1499–1513, Mar. 2014.
[7] V. Venkateswaran and A.-J. van der Veen, “Analog beamforming in MIMO communications with phase shift networks and online channel estimation,” IEEE Trans. Signal Process., vol. 58, no. 8, pp. 4131–4143, Aug. 2010.
[8] Y.-H. Yang, S.-C. Lin, and H.-J. Su, “Multiuser MIMO downlink beamforming based on group maximum SINR filtering,” IEEE Trans. Signal Process., vol. 59, no. 4, pp. 1746–1758, Apr. 2011.
[9] J. Lee, G.-T. Gil, and Y. H. Lee, “Channel estimation via orthogonal matching pursuit for hybrid MIMO systems in millimeter wave communications,” IEEE Transactions on Communications, vol. 64, no. 6, pp. 2370–2386, Jun. 2016.
[10] A. Alkhateeb, O. E. Ayach, G. Leus, and R. W. Heath Jr., “Compressed-sensing based multi-user millimeter wave systems: How many measurements are needed?” in Proc. IEEE Int. Conf. Acoustics, Speech and Sig. Process. (ICASSP). Brisbane, Australia: arXiv preprint arXiv:1505.00299, Apr 2015.
[11] A. Goldsmith, S. A. Jafar, N. Jindal, and S. Vishwanath, “Capacity limits of MIMO channels,” IEEE J. Sel. Areas Commun., vol. 21, no. 5, pp. 684–702, Jun. 2003.
[12] M. B. Shenouda and T. N. Davidson, “Convex conic formulations of robust downlink precoder design with quality of service constraints,” IEEE J. Sel. Topics Signal Process., vol. 1, no. 4, pp. 714–724, Dec. 2007.
[13] L. Liang, W. Xu, and X. Dong, “Low-complexity hybrid precoding in massive multiuser MIMO systems,” IEEE Wireless Commun. Lett., pp. 653–656, 2014.
[14] F. Sohrabi and T. N. Davidson, “Coordinate update algorithms for robust power loading for the MISO downlink with outage constraints and Gaussian uncertainties,” in Proc. Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Vancouver, Canada, May 2013, pp. 4769–4773.
[15] G. Caire, N. Jindal, M. Kobayashi, and N. Ravindran, “Multiuser MIMO achievable rates with downlink training and channel state feedback,” IEEE Trans. Inf. Theory, vol. 56, no. 6, pp. 2845–2866, 2010.
[16] K.-Y. Wang, A. M.-C. So, T.-H. Chang, W.-K. Ma, and C.-Y. Chi, “Outage constrained robust transmit optimization for multiuser MISO downlinks: Tractable approximations by conic optimization,” IEEE Trans. Signal Process., vol. 62, no. 21, pp. 5690–5705, Nov. 2014.
[17] P. Xia, R. W. Heath, and N. Gonzalez-Prelcic, “Robust analog precoding designs for millimeter wave MIMO transceivers with frequency and time division duplexing,” IEEE Trans. Commun., vol. 64, no. 11, pp. 4622–4634, Nov. 2016.
[18] B. Chalise, S. Shahbazpanahi, A. Czylwik, and A. B. Gershman, “Robust downlink beamforming based on outage probability specifications,” IEEE Trans. Wireless Commun., vol. 6, no. 10, pp. 3498–3503, 2007.
[19] M. Tshangini and M. R. Nakhai, “Second-order cone programming for robust downlink beamforming with imperfect CSI,” in Proc.IEEE Global Commun. Conf., Dec. 2013, pp. 3452–3457.
[20] N. Vucic and H. Boche, “Robust QoS-constrained optimization of downlink multiuser MISO systems,” IEEE Trans. Signal Processing, vol. 57, pp. 714–725, Feb. 2009.
[21] C. Shen, K.-Y. Wang, T.-H. Chang, Z. Qiu, and C.-Y. Chi, “Worstcase SINR constrained robust coordinated beamforming for multicell wireless systems,” in Proc. IEEE ICC, Kyoto, Japan, Jun. 5–9, 2011, pp. 1–5.
[22] M. Payaró, A. Pascual-Iserte, and M. Á. Lagunas, “Robust power allocation designs for multiuser and multiantenna downlink communication systems through convex optimization,” IEEE J. Sel. Areas Commun., vol. 25, no. 7, pp. 1390–1401, Sep. 2007.
[23] M. Joham, W. Utschick, and J. A. Nossek, “Linear transmit processing in MIMO communications systems,” IEEE Trans. Signal Process., vol. 53, no. 8, pp. 2700–2712, Aug. 2005.
[24] G. Zheng, K.-K. Wong, and B. Ottersten, “Robust cognitive beamforming with bounded channel uncertainties,” IEEE Trans. Signal Process., vol. 57, no. 12, pp. 4871–4881, Dec. 2009.
[25] L. Vandenberghe and S. Boyd, “Semidefinite programming,” SIAM Rev., vol. 38, pp. 49–95, Mar. 1996.
[26] R. A. Horn and C. R. Johnson, Matrix Analysis. New York: Cambridge Univ. Press, 1999.
[27] S. Boyd and L. Vandenberghe, Convex Optimization. New York: Cambridge Univ. Press, 2004.
[28] Y. C. Eldar and N. Merhav, “A competitive minimax approach to robust estimation of random parameters,” IEEE Trans. Signal Process., vol. 52, no. 7, pp. 1931–1946, Jul. 2004.
[29] Lofberg, J. “ YALMIP: A toolbox for modeling and optimization in MATLAB,” In Proceedings of the 2004 IEEE International Symposium on Computer Aided Control Systems Design, Sept. 2004, 284—289.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68437-
dc.description.abstract近年來,多輸入多輸出通信系統的研究一直引起關注。在毫米波多輸入多輸出通信系統中,大量的天線可以提供高速率。然而,常規的多用戶波束成型算法為每根天線配備了一個獨立的射頻鏈,由於每根天線都需要配置一個功率放大器而使其不實用,也具有相對較高的成本。因此,混合波束成型方法,其中波束形成器由低維度的數字波束成型器以及模擬波束成型器組成, 用於減少射頻鏈的數量, 同時提供可接受的性能。
本論文基於這一想法研究了多用戶大規模多輸入多輸出通信系統的混合波束成型設計。另外,考慮到在大規模天線下的可能的信道估計誤差,該問題被歸納為服務質量約束下的發射功率最小化, 同時也考慮對估量誤差的魯棒性。 我們採用最大允許均方誤差和最小容許信噪比作為約束, 并使用半定規劃來解決優化問題。 仿真結果表明,所提出的方法可以再系統中引入魯棒性的同時降低維護服務質量所需的功率。
zh_TW
dc.description.abstractThe research of massive multiple-input multiple-output (MIMO) communication system has been of interest in recent years. In millimeter wave MIMO systems, a large number of antennas can provide high data rates. However conventional multiuser MIMO beamforming algorithms equip each antenna with one independent radio frequency (RF) chain, which has a relatively high cost because of the power amplifiers each antenna element and makes it not practical. Thus hybrid beamforming (HB) method in which the overall beamformer consists of a low-dimensional digital beamformer followed by an analog beamformer has been utilized to greatly reduce the number of RF chains, while providing an acceptable performance. Here, two practical problems are emphasized, the design of a beamforming structure with less RF chains than antennas and the demand for high power efficiency. This thesis studies hybrid beamforming design for multiuser massive MIMO systems based on this idea. In addition, considering possible channel estimation under large scale antennas, the problem is formulated as the transmit power minimization under quality of service (QoS) constraint with additional consideration of the robustness against estimation errors. We adopt maximum allowable mean square errors (MSEs) and minimum tolerated signal-to-interference-plus-noise-ratios (SINRs) as QoS constraints, and use semidefinite programming (SDP) for solving the optimization problem. The simulation results demonstrate the proposed method can reduce required power to maintain QoS while introducing robustness in the system.en
dc.description.provenanceMade available in DSpace on 2021-06-17T02:21:02Z (GMT). No. of bitstreams: 1
ntu-106-R04942123-1.pdf: 731067 bytes, checksum: 07e312f9f449ae6fe633dbbc30117357 (MD5)
Previous issue date: 2017
en
dc.description.tableofcontentsChapter 1 Introduction 1
1.1 Backgrounds 1
1.2 Overview of Thesis 3
1.3 Notations 3
Chapter 2 System model and Problem Formulation 5
2.1 System model 5
2.2 Imperfect Channel State Information 8
2.3 Problem Formulation 10
Chapter 3 Hybrid Beamforming Designs 13
3.1 Robust Beamformer Design 13
3.2 Digital Precoder Design 16
3.3 Analog Precoder Design 19
3.4 Proposed Algorithm 19
Chapter 4 Simulations 21
Chapter 5 Conclusions 26
References 27
dc.language.isoen
dc.subject毫米波多輸入多輸出通信系統zh_TW
dc.subject混合波束成型設計zh_TW
dc.subject服務質量zh_TW
dc.subject魯棒性zh_TW
dc.subjectConvex Optimizationen
dc.subjectHybrid beamformingen
dc.subjectMillimeter waveen
dc.subjectQuality of serviceen
dc.subjectSemidefinite programmingen
dc.title使用凸優化的多用戶多輸入多輸出混合波束成型設計zh_TW
dc.titleRobust Hybrid Beamforming Designs for Multi-User Massive MIMO Downlinks through Convex Optimizationen
dc.typeThesis
dc.date.schoolyear105-2
dc.description.degree碩士
dc.contributor.oralexamcommittee蘇柏青,曹恆偉
dc.subject.keyword毫米波多輸入多輸出通信系統,混合波束成型設計,服務質量,魯棒性,zh_TW
dc.subject.keywordHybrid beamforming,Millimeter wave,Quality of service,Semidefinite programming,Convex Optimization,en
dc.relation.page30
dc.identifier.doi10.6342/NTU201704065
dc.rights.note有償授權
dc.date.accepted2017-08-21
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
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