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首页> 外文会议>IEEE International symposium on Personal, Indoor, and Mobile Radio Communications >Channel prediction for massive MIMO with channel compression based on principal component analysis
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Channel prediction for massive MIMO with channel compression based on principal component analysis

机译：基于主成分分析的带信道压缩的大规模MIMO信道预测

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Massive MIMO (multiple-input multiple-output) is one of the key technologies to realize 5G (5th Generation). Massive MIMO can be implemented with many antennas at a transmitter and receiver sides, and it can improve transmission quality at high frequency band by transmitting with superposing shift of radio wave toward the direction of the receiver. However, there exists an issue such as the increase of the amount of feedback of channel state information (CSI) from the receiver to the transmitter, due to the enormous number of antennas. For the purpose of solving this issue, there exists the technique to compress CSI to a lower dimension matrix and decrease the amount of feedback, by using principal component analysis (PCA). In the conventional method, the compression matrix to compress a channel matrix is calculated on the basis of PCA, and the compressed channel is fed back from the receiver to the base station (BS). In this method, the compression matrix used in PCA is generated based on the past CSI at the receiver, which leads to the degradation of transmission rate. This is because there is a mismatch between the CSI acquired at the transmitter and that when the transmitter transmits a signal, due to the channel variation during the feedback from the receiver to the transmitter. In this paper, to solve this problem, we propose the method based on PCA with the channel prediction. As the channel prediction, the forward-backward AR (Auto Regressive) model is used, and the compression matrix in PCA is generated from the predicted channels. By the computer simulation, it is shown that the system capacity is increased by generating the compression matrix from the predicted channel that improves the accuracy of channel restoration.
机译：大规模MIMO（多输入多输出）是实现5G（第五代）的关键技术之一。大规模MIMO可以在发射器和接收器侧使用许多天线来实现，并且可以通过无线电波向接收器方向的叠加移动来提高高频段的传输质量。然而，由于天线数量巨大，存在诸如从接收机到发射机的信道状态信息（CSI）的反馈量增加的问题。为了解决此问题，存在一种通过使用主成分分析（PCA）将CSI压缩为较低维矩阵并减少反馈量的技术。在常规方法中，基于PCA来计算用于压缩信道矩阵的压缩矩阵，并且将压缩的信道从接收机反馈到基站（BS）。在这种方法中，在PCA中使用的压缩矩阵是基于接收器上的过去CSI生成的，这导致了传输速率的下降。这是因为在发送器获取的CSI与发送器发送信号时的CSI之间存在不匹配，这是由于在从接收器到发送器的反馈期间的信道变化。为了解决这个问题，我们提出了一种基于PCA的信道预测方法。作为信道预测，使用前后AR（自回归）模型，并且根据预测的信道生成PCA中的压缩矩阵。通过计算机仿真显示，通过从预测通道生成压缩矩阵可以提高系统容量，从而提高通道恢复的准确性。

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