Accurate Channel State Information (CSI) is critical for maximizing throughput of massive Multi-Input Multi-Output (mMIMO) systems. Due to the environment dynamics and user mobility, CSI aging is a major challenge to achieving the large throughput potential of mMIMO. CSI prediction can be used to overcome this without increasing the overhead. Motivated by the anticipated native support for Artificial Intelligence (AI) in the fifth generation and beyond cellular standards, we propose deep learning CSI prediction solutions based on 3-Dimensional (3D) Complex Convolutional Neural Networks (CCNN). These solutions provide improved capabilities for capturing temporal and spatial correlations, enhancing CSI prediction performance. In particular, they utilize the angle delay decomposition of previously observed CSI to predict the future one. In one architecture, the network, dubbed CSI Prediction Network (CSI-PNet), uses small kernels with circular padding to efficiently capture the correlation between propagation paths in the angle domain. This architecture can be further improved by the use of an attention-like model to vary the weights and enhance prediction performance adaptively. We also propose methods to enhance robustness to noise, time-, and frequency offsets. We tested these solutions using both 3GPP-compatible simulations and field measurement in commercial network. Our solutions demonstrate stable performance and significantly outperform several benchmarks, especially at low and medium speeds. They strike a balance between the performance and the architecture complexity, indicating suitability for actual implementation.

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10286287