A Multi-step Channel Prediction Method Based on Pseudo-3D Convolutional Neural Network with Attention Mechanism

TAO Jing; HOU Meng; PENG Wei; ZHANG Guoyan; DAI Jiaming; LIU Weiming; WANG Haidong; WANG Zhen

doi:10.11999/JEIT251090

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TAO Jing, HOU Meng, PENG Wei, ZHANG Guoyan, DAI Jiaming, LIU Weiming, WANG Haidong, WANG Zhen. A Multi-step Channel Prediction Method Based on Pseudo-3D Convolutional Neural Network with Attention Mechanism[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251090

Citation:

TAO Jing, HOU Meng, PENG Wei, ZHANG Guoyan, DAI Jiaming, LIU Weiming, WANG Haidong, WANG Zhen. A Multi-step Channel Prediction Method Based on Pseudo-3D Convolutional Neural Network with Attention Mechanism[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251090

Citation:

TAO Jing, HOU Meng, PENG Wei, ZHANG Guoyan, DAI Jiaming, LIU Weiming, WANG Haidong, WANG Zhen. A Multi-step Channel Prediction Method Based on Pseudo-3D Convolutional Neural Network with Attention Mechanism[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251090

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A Multi-step Channel Prediction Method Based on Pseudo-3D Convolutional Neural Network with Attention Mechanism

doi: 10.11999/JEIT251090 cstr: 32379.14.JEIT251090

1.
China Electric Power Research Institute, Beijing 100192, China
2.
School of Communications and Information Engineering, Nanijing University of Posts and Telecommunications, Nanjing 210003, China
3.
School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430074, China
4.
Chifeng Power Supply Company, State Grid Inner Mongolia Eastern Electric Power Co., Ltd, Chifeng 024099, China

Funds: The State Grid Corporation of China Headquarters Science and Technology Project (1400-202455422A-3-5-YS)

Received Date: 2025-10-13
Accepted Date: 2025-12-02
Rev Recd Date: 2025-11-26

Available Online: 2025-12-09

Abstract

Abstract

Objective With the rapid growth in connections and data traffic in Fifth Generation (5G) mobile networks, massive Multiple-Input Multiple-Output (MIMO) has become a key technology for improving network performance. The spectral efficiency and energy efficiency of massive MIMO transmission depend on accurate Channel State Information (CSI). However, the non-stationary characteristics of wireless channels, terminal processing delay, and the use of ultra-high-frequency bands intensify CSI aging, which necessitates channel prediction. Most mainstream prediction schemes are designed for generalized stationary channels and rely on single-step prediction. In non-stationary environments, CSI obtained through single-step prediction is likely to become outdated, and frequent single-step prediction greatly increases pilot overhead. To address these challenges, a multi-step channel prediction method based on a Pseudo-Three-Dimensional Convolutional Neural Network (P3D-CNN) and an attention mechanism is proposed. The method learns the joint time–frequency characteristics of CSI, leverages high frequency-domain correlation to mitigate the effect of lower time-domain correlation in multi-step prediction, and improves prediction performance. Methods In this study, the uplink model of a massive MIMO system is constructed (Fig. 1). CSI is obtained through channel estimation, using an Inverse Fast Fourier Transform (IFFT) at the transmitter and a Fast Fourier Transform (FFT) at the receiver. Actual channel measurements provide a CSI dataset with time–frequency dimensions, and autocorrelation analyses are performed in both domains. A multi-step channel prediction network, termed P3D-CNN with the Convolutional Block Attention Module (CBAM) (Fig. 13), is designed. The P3D-CNN structure replaces the traditional Three-Dimensional Convolutional Neural Network (3D-CNN) by decomposing the three-dimensional convolution into a two-dimensional convolution in the frequency domain and a one-dimensional convolution in the time domain, which greatly reduces computational complexity. The CBAM-based hybrid attention mechanism is incorporated to extract global information in the frequency and channel domains, further improving channel prediction accuracy. Results and Discussions Based on the measured CSI dataset, the prediction method using an AutoRegressive (AR) model, the prediction method using Fully Connected Long Short-Term Memory (FC-LSTM), and the prediction method using P3D-CNN-CBAM are compared under different prediction steps. Simulation results show that the average Normalized Mean Square Error (NMSE) of the proposed P3D-CNN-CBAM method is lower than that of the other two methods (Fig. 17). As the prediction step increases from 1 to 10, prediction error rises sharply because the AR model and FC-LSTM rely solely on time-domain correlation. When the prediction step is 10, the average NMSE of these two methods reaches 0.5868 and 0.7648, respectively. The P3D-CNN-CBAM method yields an average NMSE of only 0.3078, maintaining strong prediction performance. The improvement brought by integrating CBAM into the P3D-CNN network is also verified (Fig. 18). Finally, through transfer learning, the proposed method is extended from single-day datasets to multi-day scenarios. Conclusions Based on the measured CSI dataset, a multi-step prediction method addressing CSI aging in massive MIMO systems is proposed. The method applies P3D-CNN with CBAM to improve multi-step prediction accuracy. By replacing full three-dimensional convolution with pseudo-three-dimensional convolution, time–frequency CSI information is effectively extracted, and the CBAM mechanism enhances the learning of global features. Experimental results show that: (1) the proposed method achieves clear performance advantages over AR- and FC-LSTM-based approaches; and (2) through transfer learning, multi-step prediction is extended from single-antenna to multi-antenna scenarios.

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References(17)

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