论文标题
Terahertz宽带大型MIMO系统的近场梁的深入学习
Deep Learning of Near Field Beam Focusing in Terahertz Wideband Massive MIMO Systems
论文作者
论文摘要
采用大型天线阵列并利用大带宽具有将非常高的数据速率带到未来的无线通信系统中。但是，这将系统带入了近场政权，也使传统的收发器体系结构遭受宽带效应的影响。为了解决这些问题，在本文中，我们提出了一种低复杂性频率波束形成解决方案，该解决方案是为混合时间延迟和基于相位转移的RF体系结构而设计的。为了降低复杂性，将时间延迟和相移的联合设计问题分解为两个子问题，其中提出了信号模型启发的在线学习框架，以了解量化的模拟相位变速器的变化，以及低复杂的几何学辅助方法，以配置时间延迟设置的时间延迟设置。仿真结果突出了所提出的解决方案在大型天线阵列系统中实现较宽范围内稳健性能的功效。
Employing large antenna arrays and utilizing large bandwidth have the potential of bringing very high data rates to future wireless communication systems. However, this brings the system into the near-field regime and also makes the conventional transceiver architectures suffer from the wideband effects. To address these problems, in this paper, we propose a low-complexity frequency-aware beamforming solution that is designed for hybrid time-delay and phase-shifter based RF architectures. To reduce the complexity, the joint design problem of the time delays and phase shifts is decomposed into two subproblems, where a signal model inspired online learning framework is proposed to learn the shifts of the quantized analog phase shifters, and a low-complexity geometry-assisted method is leveraged to configure the delay settings of the time-delay units. Simulation results highlight the efficacy of the proposed solution in achieving robust performance across a wide frequency range for large antenna array systems.
