Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the "curse of mobility". The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD) wideband beamformer is proposed for channel training. Our idea consists in the exploitation of the partial channel reciprocity of FDD and the angle-delay-Doppler channel structure. More precisely, the base station (BS) estimates the angle-delay-Doppler information of the UL channel based on UL pilots using Matrix Pencil method. It then computes the wideband JADD beamformers according to the extracted parameters. Afterwards, the user estimates and feeds back some scalar coefficients for the BS to reconstruct the predicted DL channel. Asymptotic analysis shows that the CSI prediction error converges to zero when the number of BS antennas and the bandwidth increases. Numerical results with industrial channel model demonstrate that our framework can well adapt to high speed (350 km/h), large CSI delay (10 ms) and channel sample noise.
翻译：据信,在5G和5G以上地区,大规模多投入多重输出(MIMO)被认为能够提供无人代表的光谱效率增益。然而,在商业部署期间,即所谓的“流动性诅咒”期间,出现了实际的挑战。当用户速度提高时,大型MIMO的表现令人震惊地下降。在本文中,我们用一个新的频道国家信息(CSI)获取框架来解决频率分解(DFD)大规模多投入多重产出(MIMO)的问题。为频道培训提议了一个联合角度-延迟-多普勒(JADDD)宽频谱断面断面。我们的想法是利用DDDFD的局部通道对等性和角-交替-多普勒通道结构。更准确地说,基础站(BS)估计了基于UL试点项目的角对角调频-多普勒信息。然后根据提取的参数对宽频带JADDD显示。随后,用户对BS为重建所预测的DL频道和角度-DUPL频道对高频带速度框架的精确度(C-S)分析结果显示,CS的CRestrodal 和CS结果显示,这时,C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-L-L-C-C-C-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L
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