Employing large antenna arrays and utilizing large bandwidth have the potential of bringing very high data rates to future wireless communication systems. To achieve that, however, new challenges associated with these systems need to be addressed. First, the large array aperture brings the communications to the near-field region, where the far-field assumptions no longer hold. Second, the analog-only (phase shifter based) beamforming architectures result in performance degradation in wideband systems due to their frequency unawareness. To address these problems, this paper proposes a low-complexity frequency-aware near-field beamforming framework for hybrid time-delay (TD) and phase-shifter (PS) based RF architectures. Specifically, a \textit{signal model inspired online learning} framework is proposed to learn the phase shifts of the quantized analog phase-shifters. Thanks to the model-inspired design, the proposed learning approach has fast convergence performance. Further, a low-complexity \textit{geometry-assisted} method is developed to configure the delay settings of the TD units. Simulation results highlight the efficacy of the proposed solution in achieving robust near-field beamforming performance for wideband large antenna array systems.
翻译:使用大型天线阵列和使用大型带宽具有为未来的无线通信系统带来非常高的数据率的潜力。然而,为了实现这一点,需要应对与这些系统相关的新挑战。首先,大型阵列孔径将通信带到远处假设不再能维持的近地点区域。第二,只使用模拟的(以级轮班为基础的)波形结构由于频率不知情而导致宽带系统的性能退化。为解决这些问题,本文件提议为基于时差的混合和分阶段变换的RF结构制定一个低复杂性频率近场调整框架。具体地说,提议了一个\textit{信号模型激励在线学习}框架,以学习四分化的模拟阶段变换的阶段性转变。由于模型启发设计,拟议学习方法的性能迅速趋同。此外,还开发了一个低兼容性\textit{地理测量-辅助}方法,以配置基于时差(TD)和阶段变换(PS)的RF结构的延迟设置。具体地,建议采用“TTD-delay (TD)) 和分阶段变换(PS) 结构的低兼容性框架框架。Simlating 成果突出显示拟议的宽度解决方案的效能,以在近地面上实现强的大型变换式系统。