Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach
Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach
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DOI：
10.1109/tnse.2018.2848960
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发表时间：
2020-01-01
影响因子：
6.6
通讯作者：
Li, Pan
中科院分区：
文献类型：
--
作者：
Luo, Changqing;Ji, Jinlong;Li, Pan
信道状态信息估计是无线通信系统中最基本的问题之一。到目前为止，已经发展了各种方法来进行CSI估计。然而，它们通常需要很高的计算复杂性，这使得它们不适合5G无线通信，因为它们采用了许多新技术（例如，大规模MIMO， OFDM和毫米波（mmWave））。在本文中，我们提出了一种有效的在线CSI预测方案，称为OCEAN，用于从5G无线通信系统的历史数据预测CSI。具体来说，我们首先确定影响无线电链路CSI的几个重要特征，并由特征信息和CSI组成数据样本。然后，我们设计了一个学习框架，该框架是CNN（卷积神经网络）和长短期记忆（LSTM）网络的集成。我们还进一步开发了离线-在线两步训练机制，使预测结果在应用于实际5G无线通信系统时更加稳定。为了验证OCEAN的有效性，我们考虑了四个典型的案例研究，并在四个场景下进行了广泛的实验，即两个室外和两个室内场景。实验结果表明，OCEAN不仅可以快速得到预测的CSI值，而且可以实现高准确度的CSI预测，预测值与实测值之间的平均差比（ADR）可达2.650 ~ 3.457%。
Channel state information (CSI) estimation is one of the most fundamental problems in wireless communication systems. Various methods, so far, have been developed to conduct CSI estimation. However, they usually require high computational complexity, which makes them unsuitable for 5G wireless communications due to employing many new techniques (e.g., massive MIMO, OFDM, and millimeter-Wave (mmWave)). In this paper, we propose an efficient online CSI prediction scheme, called OCEAN, for predicting CSI from historical data in 5G wireless communication systems. Specifically, we first identify several important features affecting the CSI of a radio link and a data sample consists of the information of the features and the CSI. We then design a learning framework that is an integration of a CNN (convolutional neural network) and a long short term with memory (LSTM) network. We also further develop an offline-online two-step training mechanism, enabling the prediction results to be more stable when applying it to practical 5G wireless communication systems. To validate OCEAN's efficacy, we consider four typical case studies, and conduct extensive experiments in the four scenarios, i.e., two outdoor and two indoor scenarios. The experiment results show that OCEAN not only obtains the predicted CSI values very quickly but also achieves highly accurate CSI prediction with up to 2.650-3.457 percent average difference ratio (ADR) between the predicted and measured CSI.