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SINR Constrained Beamforming for a MIMO Multi-User Downlink System: Algorithms and Convergence Analysis

机译:<?Pub _newline?> MIMO多用户下行链路系统的SINR约束波束成形:算法和收敛性分析

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Consider a multiple-input multiple-output (MIMO) downlink multi-user channel. A well-studied problem in such a system is the design of linear beamformers for power minimization with the quality of service (QoS) constraints. The most representative algorithms for solving this class of problems are the so-called minimum mean square error (MMSE)–second-order cone programming (SOCP) algorithm [Visotksy and Madhow, “Optimum Beamforming Using Transmit Antenna Arrays,” Proc. IEEE Veh. Technol. Conf., May 1999, vol. 1, pp. 851–856] , [Wong, Zheng, and Ng, “Convergence Analysis of Downlink MIMO Antenna System Using Second-Order Cone Programming,” Proc. 62nd IEEE Veh. Technol. Conf., Sep. 2005, pp. 492–496] and the uplink-downlink duality (UDD) algorithm [Codreanu, Tolli, Juntti, and Latva-Aho, “Joint Design of Tx-Rx Beamformers in MIMO Downlink Channel,” IEEE Trans. Signal Process., vol. 55, no. 9, pp. 4639–4655, Sep. 2007]. The former is based on alternating optimization of the transmit and receive beamformers; while the latter is based on the well-known uplink-dowlink duality theory. Despite their wide applicability, the convergence to Karush–Kuhn–Tucker (KKT) solutions of both algorithms is still open in the literature. In this paper, we rigorously establish the convergence of these algorithms for QoS-constrained power minimization (QCPM) problem with both single stream and multiple streams per user cases. Key to our analysis is the development and analysis of a new MMSE-DUAL algorithm, which connects the MMSE-SOCP and the UDD algorithm. Our numerical experiments show that 1) all these algorithms can almost always reach points with the same objective value irrespective of initialization and 2) the MMSE-SOCP/MMSE-DUAL algorithm works well while the UDD algorithm may fail with an infeasible initialization.
机译:考虑多输入多输出(MIMO)下行链路多用户信道。在这样的系统中,一个经过充分研究的问题是设计线性波束形成器,以在服务质量(QoS)约束下实现功耗最小化。解决此类问题的最有代表性的算法是所谓的最小均方误差(MMSE)–二阶锥规划(SOCP)算法[Visotksy和Madhow,“使用发射天线阵列的最佳波束成形”,Proc.Natl.Acad.Sci.USA,87:3877-2404。 IEEE Veh。技术。 Conf。,1999年5月,第一卷。 1,第851–856页,[Wong,Zheng和Ng,“使用二阶锥规划的下行MIMO天线系统的收敛性分析”,Proc.Natl.Acad.Sci.USA,88,1593-1,第5页。第62 IEEE Veh。技术。 Conf。,2005年9月,第492–496页]和上行链路-下行链路对偶性(UDD)算法[Codreanu,Tolli,Juntti和Latva-Aho,“ MIMO下行链路信道中Tx-Rx波束形成器的联合设计”,IEEE反式信号处理,第一卷55,不。 9,第4639-4655页,2007年9月]。前者基于发射和接收波束形成器的交替优化;而后者则基于众所周知的上行链路-下行链路对偶理论。尽管它们具有广泛的适用性,但在两种文献中仍然都可以收敛到Karush–Kuhn–Tucker(KKT)解决方案。在本文中,我们针对每个用户案例针对单流和多流同时针对QoS约束的功率最小化(QCPM)问题,严格建立了这些算法的收敛性。我们分析的关键是开发和分析新的MMSE-DUAL算法,该算法将MMSE-SOCP和UDD算法联系起来。我们的数值实验表明,1)所有这些算法几乎都可以始终达到具有相同目标值的点,而与初始化无关,并且2)MMSE-SOCP / MMSE-DUAL算法运行良好,而UDD算法可能会因初始化不可行而失败。

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