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Weighted Sum Rate Maximization for Multi-User MISO Systems with Low Resolution Digital to Analog Converters

机译:具有低分辨率数模转换器的多用户MISO系统的加权总和速率最大化

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We study the problem of downlink beamforming for the Weighted Sum Rate maximization (WSR) of Multi-User Multiple-Input-Single-Output systems with low-resolution Digital-to-Analog Converters (DACs) in a single-cell setup. The DACs, modeled as quantizers, are performing a nonlinear operation on the signals and are linearized using Bussgang decomposition and a linear approximation of the covariance of quantized signals. For the maximization of the WSR of the linearized system, we propose a gradient-based solution and a lower-complexity heuristic solution, based on the structure of the globally optimal solution. Through numerical simulations, we show that taking quantization into account in the filter design results in significant performance improvement when the number of transmit antennas is comparable to the number of users. When the number of transmit antennas becomes much larger than the number of users, it is found that the heuristic solution achieves near-optimal performance and that a quantization-aware design becomes less important.
机译:我们在单单元设置中研究了具有低分辨率数模转换器(DAC)的多用户多输入单输出系统的加权总和速率最大化(WSR)的下行链路波束成形问题。建模为量化器的DAC对信号执行非线性运算,并使用Bussgang分解和量化信号协方差的线性近似进行线性化。为了最大化线性化系统的WSR,我们基于全局最优解的结构,提出了基于梯度的解决方案和较低复杂度的启发式解决方案。通过数值模拟,我们表明,当发射天线的数量与用户数量相当时,在滤波器设计中考虑量化会带来显着的性能改进。当发射天线的数量变得比用户数量大得多时,发现启发式解决方案可实现近乎最佳的性能,而量化感知设计的重要性则降低。

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