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Optimal Training Sequences for Large-Scale MIMO-OFDM Systems

机译:大规模MIMO-OFDM系统的最佳训练序列

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This paper considers the optimal design of training sequences for channel estimation in large-scale multiple-input multiple-output orthogonal frequency-division multiplexing systems. The application scenario of interest is when the number of transmit antennas for the downlink (or the number of receive antennas for the uplink) is large, but not large enough to benefit the asymptotical optimality of using equipower training sequences (e.g., due to practical constraints on deployment costs, space, and antenna size). Under the criterion of minimizing the mean square error of the channel estimate, the optimal design of training sequences for such systems poses a truly large-scale optimization problem, to which existing optimization solvers are not applicable. We develop a fast convex programming (FCP) procedure to find its global optimal solution. In each iteration of the proposed FCP procedure, a solution is found in a scalable and closed form. The singularity and ill-conditionedness of the channel correlation matrices are also exploited to improve the computation efficiency. Furthermore, we also examine the design of reduced-length training sequences and develop a successive quadratic programming procedure to find the solutions. Intensive simulation results are provided to illustrate the performance of our methods.
机译:本文考虑了大规模多输入多输出正交频分复用系统中信道估计训练序列的优化设计。感兴趣的应用场景是当下行链路的发射天线数量(或上行链路的接收天线数量)很大但又不足以使使用等功率训练序列的渐近最优时(例如,由于实际限制)部署成本,空间和天线尺寸)。在最小化信道估计的均方误差的标准下,这种系统的训练序列的最佳设计提出了一个真正的大规模优化问题,现有的优化求解器不适用。我们开发了一种快速凸规划(FCP)程序来查找其全局最优解。在提出的FCP程序的每次迭代中,都找到了可扩展且封闭形式的解决方案。信道相关矩阵的奇异性和病态性也被用来提高计算效率。此外,我们还检查了长度减少的训练序列的设计,并开发了一个连续的二次编程程序来找到解决方案。提供了密集的仿真结果来说明我们方法的性能。

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