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研究生: 翁穫勝
Hou-Sheng Weng
論文名稱: 利用連續凸近似最佳化方法之多用戶強健性波束成形設計
Worst-Case Robust Multiuser Beamforming by Successive Convex Approximation
指導教授: 張縱輝
Tsung-Hui Chang
口試委員: 林士駿
Shih-Chun Lin
王煥宗
Huan-Chun Wang
學位類別: 碩士
Master
系所名稱: 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年: 2015
畢業學年度: 103
語文別: 中文
論文頁數: 57
中文關鍵詞: 波束成形連續凸近似多使用者
外文關鍵詞: Beamforming, Successive Convex Approximation, Multiuser
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  • 本論文考慮一個多用戶無線通訊系統的下行鏈波束成形設計問題,目標於分別最大化用戶的頻譜效益(Spectrum Efficiency)與系統的能量效益(Energy Efficiency)。具體而言,我們考慮基地台存在有界的通道估測誤差並且設計能最大化系統最差頻譜效益與最差能量效益的強健式波束成形設計。這個問題已知是NP-hard 的,因此我們尋找有效率且高精準性的次佳解。我們提出的低複雜度演算法是將原本困難非凸的最佳化問題,以一連串的凸近似問題取代,逐步尋找高效能近似解。不論是頻譜效益或能量效益,模擬結果皆顯示本論文提出的方法比目前現有的方法亦或是提供更好的性能或是具有較低的計算複雜度。


    This work considers multiuser downlink beamforming design problems for maximizing the system spectrum efficiency and energy efficiency, respectively. Specifically, we assume that the base station has only imperfect, but bounded channel state information (CSI) error, and aim to design beamforming strategies for maximizing the worst-case spectrum efficiency and energy efficiency, respectively. The considered problems have been known to be NP-hard in general. Therefore, we seek computationally efficient and high-quality approximate solutions to the considered problems. The proposed methods are based on the successive convex approximation techniques, in which the difficult problem is approximated by a sequence of convex problems. The presented simulation results demonstrate that the proposed methods either outperform the existing methods or are computationally more efficient.

    第一章 緒論 第二章 系統模型與問題表述 第三章 強健頻譜效益演算法 第四章 強健能量效益演算法 第五章 模擬結果 第六章 結論

    [1] K.-Y. Wang, H. Wang, Z. Ding, and C.-Y. Chi, “A low-complexity algorithm for worstcase utility maximization in multiuser MISO downlink,” IEEE 78th Vehicular Technology Conference, 2-5 Sept. 2013.

    [2] D. W. K. Ng, E. S. Lo, and R. Schober, “Energy-efficient power allocation in OFDM systems with wireless information and power transfer,” IEEE International Conference on Communications (ICC), 9-13 June 2013.

    [3] B. D. V. Veen and K. M. Buckley, “Beamforming: a versatile approach to spatial filtering,” IEEE ASSP Magazine, vol. 5, no. 2, pp. 4–24, April 1988.

    [4] A. B. Gershman, N. D. Sidiropoulos, S. Shahbazpanahi, M. Bengtsson, and B. Ottersten, “Convex Optimization Based Beamfoming,” IEEE Signal Processing Magazine, vol. 27, no. 3, pp. 62–75, May 2010.

    [5] L. Liu, R. Zhang, and K.-C. Chua, “Achieving global optimality for weighted sum-rate maximization in the K-user Gaussian interference channel with multiple antennas,” IEEE Transactions on Wireless Communications, vol. 11, no. 5, pp. 1933–1945, May 2012.

    [6] S. K. Joshi, P. C. Weeraddana, M. Codreanu, and M. Latva-aho, “Weighted sumrate maximization for MISO downlink cellular networks via branch and bound,” IEEE Transactions on Signal Processing, vol. 60, no. 4, pp. 2090–2095, April 2012.

    [7] E. Björnson, G. Zheng, M. Bengtsson, and B. Ottersten, “Robust monotonic optimization framework for multicell MISO systems,” IEEE Transactions on Signal Processing, vol. 60, no. 5, pp. 2508–2523, May 2012.

    [8] Z.-Q. Luo and S. Zhang, “Dynamic Spectrum Management: Complexity and Duality,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 57–73, Feb. 2008.

    [9] C. T. K. Ng and H. Huang, “Linear precoding in cooperative MIMO cellular networks with limited coordination clusters,” IEEE Journal on Selected Areas in Communications, vol. 28, no. 9, pp. 1446–1454, Dec. 2010.

    [10] Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, “An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,” IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4331–4340, Sep. 2011.

    [11] L.-N. Tran, M. F. Hanif, A. Tölli, and M. Juntti, “Fast converging algorithm for weighted sum rate maximization in multicell MISO downlink,” IEEE Transactions on Signal Processing, vol. 19, no. 12, pp. 872–875, Dec. 2012.

    [12] D. J. Love, R. W. H. Jr, V. K. N. Lau, D. Gesbert, B. D. Rao, and M. Andrews, “An overview of limited feedback in wireless communication systems,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 8, pp. 1341–1365, Oct. 2008.

    [13] 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 Transactions on Signal Processing, vol. 62, no. 21, pp. 5690–5705, Nov. 2014.

    [14] C. Shen, T.-H. Chang, K.-Y. Wang, Z. Qiu, and C.-Y. Chi, “Distributed robust multicell coordinated beamforming with imperfect CSI:an ADMM approach,” IEEE Transactions on Signal Processing, vol. 60, no. 6, pp. 2988–3003, June 2012.

    [15] A. Wiesel, Y. C. Eldar, and S. Shamai(Shitz), “Optimization of the MIMO compound capacity,” IEEE Transactions on Wireless Communications, vol. 6, no. 3, pp. 1094–1101, Mar. 2007.

    [16] Z.-Q. Luo, W.-K. Ma, A. M.-C. So, Y. Ye, and S. Zhang, “Semidefinite relaxation of quadratic optimization problems,” IEEE Signal Processing Magazine, vol. 27, no. 3, pp. 20–34, May 2010.

    [17] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press,2004.

    [18] W. Dinkelbach, “On nonlinear fractional programming,” Management Science, vol. 13, pp. 492–498, Mar. 1967.

    [19] A. Shaverdian and M. R. Nakhai, “Robust Distributed Beamforming With Interference Coordination in Downlink Cellular Networks,” IEEE Transactions on Wireless Communications, vol. 62, no. 7, pp. 2411–2420, July 2014.

    [20] D. W. K. Ng, E. S. Lo, and R. Schober, “Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas,” IEEE Transactions on Wireless Communications, vol. 11, no. 9, pp. 3292–3304, Sept. 2012.

    [21] M. Grant and S. Boyd. (2009, June) CVX: Matlab software for disciplined convex programming. [Online]. Available: http://cvxr.com/cvx/

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