Shared Angles-of-Departure in Massive MIMO Channels: Correlation Analysis and Performance Impact
Shared Angles-of-Departure in Massive MIMO Channels: Correlation Analysis and Performance Impact
复制标题
大规模 MIMO 信道中的共享出发角：相关性分析和性能影响
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
A. Sabharwal
中科院分区：
文献类型：
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作者：
Xu Du;A. Sabharwal
在实际环境中，近期的大规模多输入多输出（MIMO）测量表明用户信道可能是相关的。在本文中，我们研究了由共享出发角引起的用户信道相关性。我们首先推导了大阵元体制下的用户相关性分布，然后利用大阵元的实际测量来检验用户相关性。作为一种数据驱动的观察结果，我们发现所有邻近用户的相关性高于0.4，并且当基站天线数量$M$增加到超过36根天线时，相关性几乎没有降低。此外，近三分之一的用户，即使他们相隔数十个波长，其相关性也是独立同分布（i.i.d.）瑞利衰落模型相关性的两倍以上。最后，我们描述了用户相关性对系统性能的影响。随着$M$的增加，由于信道相关，共轭波束成形系统遭受线性增长的用户间干扰。然而，对于迫零波束成形系统，用户间干扰是一个常数，不会随$M$增加。特别是，迫零波束成形系统可以服务数量线性增加的相关用户，并且随着$M$的增加，系统可实现速率呈线性增长。因此，对相关用户进行空间复用可能是一种有吸引力的大规模MIMO设计。
In practical environments, recent massive MIMO measurements demonstrate that user channels can be correlated. In this paper, we study the user channel correlation induced by shared angles-of-departure. We first derive the user correlation distribution in the large array regime, and then examine the user correlation using actual measurements from a large array. As a data-driven observation, we discover that the correlation of all close-by users is higher than $0.4$ and barely reduces as the number of base-station antennas $M$ increases beyond $36$ antennas. Furthermore, nearly one-third of users, even when they are tens of wavelengths apart, have a correlation that is more than twice the correlation of an i.i.d. Rayleigh fading model. Lastly, we characterize the impact of user correlation on system performance. As $M$ increases, conjugate beamforming systems suffer a linearly growing inter-user interference due to correlated channels. However, for zero-forcing beamforming systems, the inter-user interference is a constant that does not increase with M. In particular, zero-forcing beamforming systems can serve a linearly increasing number of correlated users and achieve a linear growth in the system achievable rate as $M$ increases. Hence, spatial multiplexing correlated users can be an attractive massive MIMO design.
平衡排队和重传：延迟优化大规模 MIMO 设计
影响因子：
10.4
作者：
Du, Xu;Sun, Yin;Shroff, Ness B.;Sabharwal, Ashutosh
通讯作者：
Sabharwal, Ashutosh