Abdul-Hadi Alaa M, Abdulrazzaq Naser Marwah, Alsabah Muntadher, Abdulhussain Sadiq H, Mahmmod Basheera M
Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad, Iraq.
Department of Architectural Engineering, University of Baghdad, Al-Jadriya, Baghdad, Iraq.
PeerJ Comput Sci. 2022 Jun 21;8:e1017. doi: 10.7717/peerj-cs.1017. eCollection 2022.
Massive multiple-input multiple-output (massive-MIMO) is considered as the key technology to meet the huge demands of data rates in the future wireless communications networks. However, for massive-MIMO systems to realize their maximum potential gain, sufficiently accurate downlink (DL) channel state information (CSI) with low overhead to meet the short coherence time (CT) is required. Therefore, this article aims to overcome the technical challenge of DL CSI estimation in a frequency-division-duplex (FDD) massive-MIMO with short CT considering five different physical correlation models. To this end, the statistical structure of the massive-MIMO channel, which is captured by the physical correlation is exploited to find sufficiently accurate DL CSI estimation. Specifically, to reduce the DL CSI estimation overhead, the training sequence is designed based on the eigenvectors of the transmit correlation matrix. To this end, the achievable sum rate (ASR) maximization and the mean square error (MSE) of CSI estimation with short CT are investigated using the proposed training sequence design. Furthermore, this article examines the effect of channel hardening in an FDD massive-MIMO system. The results demonstrate that in high correlation scenarios, a large loss in channel hardening is obtained. The results reveal that increasing the correlation level reduces the MSE but does not increase the ASR. However, exploiting the spatial correction structure is still very essential for the FDD massive-MIMO systems under limited CT. This finding holds for all the physical correlation models considered.
大规模多输入多输出（massive-MIMO）被视为满足未来无线通信网络对数据速率巨大需求的关键技术。然而，为使大规模MIMO系统实现其最大潜在增益，需要具有足够低开销以适应短相干时间（CT）的足够准确的下行链路（DL）信道状态信息（CSI）。因此，本文旨在克服在具有短CT的频分双工（FDD）大规模MIMO中DL CSI估计的技术挑战，考虑了五种不同的物理相关模型。为此，利用由物理相关性捕获的大规模MIMO信道的统计结构来找到足够准确的DL CSI估计。具体而言，为降低DL CSI估计开销，基于发射相关矩阵的特征向量设计训练序列。为此，使用所提出的训练序列设计研究了具有短CT的CSI估计的可达和速率（ASR）最大化和均方误差（MSE）。此外，本文研究了FDD大规模MIMO系统中信道硬化的影响。结果表明，在高相关场景中，信道硬化会有很大损失。结果表明，增加相关水平会降低MSE但不会增加ASR。然而，在有限CT下，利用空间校正结构对于FDD大规模MIMO系统仍然非常重要。这一发现适用于所考虑的所有物理相关模型。
