[2510.12996] CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing

Computer Science > Machine Learning arXiv:2510.12996 (cs) [Submitted on 14 Oct 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing Authors:Sikai Cheng, Reza Zandehshahvar, Haoruo Zhao, Daniel A. Garcia-Ulloa, Alejandro Villena-Rodriguez, Carles Navarro Manchón, Pascal Van Hentenryck View a PDF of the paper titled CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing, by Sikai Cheng and 6 other authors View PDF HTML (experimental) Abstract:Channel state information (CSI) prediction is a promising strategy for ensuring reliable and efficient operation of massive multiple-input multiple-output (mMIMO) systems by providing timely downlink (DL) CSI. While deep learning-based methods have advanced beyond conventional model-driven and statistical approaches, they remain limited in robustness to practical non-Gaussian noise, generalization across diverse channel conditions, and computational efficiency. This paper introduces CSI-4CAST, a hybrid deep learning architecture that integrates 4 key components, i.e., Convolutional neural network residuals, Adaptive correction layers, ShuffleNet blocks, and Transformers, to efficiently capture both local and long-range dependencies in CSI prediction. To enable rigorous evaluation, this work further presents a comprehensive benchmark, CSI-RRG for Regular,...