Dealing with the Mobility Problem of Massive MIMO using Extended Prony’s Method

Haifan Yin, Haiquan Wang, Yingzhuang Liu, D. Gesbert
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引用次数: 3

Abstract

Massive MIMO is a key technology for 5th generation (5G) mobile communications. The large excess of base station (BS) antennas brings unprecedented spectral efficiency. However, during the initial phase of industrial testing, a practical challenge arises which undermines the actual deployment of massive MIMO and is related to mobility. In fact, testing teams reported that in moderate-mobility scenarios, e.g., 30 km/h of UE speed, the performance may drop 50% compared to the low-mobility scenario, a problem not foreseen by theoretical papers on the subject. In order to deal with this challenge, we propose a Prony-based angular-delay domain (PAD) prediction method, which is built on exploiting the angle-delay-Doppler structure of the multipath. Our theoretical analysis shows that when the number of base station antennas and the bandwidth are large, the prediction error of our PAD algorithm converges to zero for any UE velocity level, provided that only two accurate enough previous channel samples are available. Simulation results show that under the realistic channel model of 3GPP in rich scattering environment, our proposed method even approaches the performance of stationary scenarios where the channels do not vary at all.
用扩展proony方法处理大规模MIMO的移动性问题
大规模MIMO是第五代(5G)移动通信的关键技术。基站天线的大量过剩带来了前所未有的频谱效率。然而,在工业测试的初始阶段,出现了一个实际的挑战,它破坏了大规模MIMO的实际部署,并与移动性有关。事实上,测试团队报告说,在中等机动性的情况下,例如在30公里/小时的UE速度下,性能可能会比低机动性的情况下降50%,这是有关该主题的理论论文没有预见到的问题。为了应对这一挑战,我们提出了一种基于多径角延迟多普勒结构的角延迟域(PAD)预测方法。我们的理论分析表明,当基站天线数量和带宽较大时,只要有两个足够精确的前信道样本,我们的PAD算法对任何UE速度水平的预测误差都收敛于零。仿真结果表明,在丰富散射环境下的3GPP真实信道模型下,我们提出的方法甚至接近信道完全不变化的静态场景的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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