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

机译:具有低分辨率数字到模拟转换器的多用户MISO系统的加权和速率最大化

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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:我们研究了在单个小区设置中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和速率最大化(WSR)的下行链路波束形成的问题。模型为量化器的DAC正在对信号进行非线性操作,并且使用BUSSGANG分解和量化信号协方差的线性近似。对于线性化系统的WSR的最大化,我们提出了一种基于梯度的解决方案和较低复杂性启发式解决方案,基于全球最佳解决方案的结构。通过数值模拟,我们显示在滤波器设计中考虑量化,导致当发射天线的数量与用户数相当时,在显着的性能改进。当发射天线的数量变得远远大于用户数量时,发现启发式解决方案实现了近最佳性能,并且量化感知设计变得不太重要。

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