Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach - Luo, Changqing; Ji, Jinlong; Wang, Qianlong; Chen, Xuhui; Li, Pan (IEEE transactions on network science and engineering, 2020)
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.