Autonomous Self-Trained Channel State PredictionMethod for mmWave Vehicular Communications

Publication Type

Conference Proceeding

Name of Author

• Abidemi Orimogunje, African Center of Excellence in IoT, University of Rwanda, RwandaFollow
• Vukan Ninkovic, The Institute for Artificial Intelligence Research and Development of Serbia, Serbia
• Evariste Twahirwa, African Center of Excellence in IoT, University of Rwanda, Rwanda
• Gaspard Gashema, African Center of Excellence in IoT, University of Rwanda, Rwanda
• Dejan Vukobratovic, Faculty of Technical Sciences, University of Novi Sad, Serbia

Publication Date (Issue Year)

2026

Journal Name

Electrical Engineering and Systems Science

Abstract

Establishing and maintaining 5G mmWave vehicular connectivity poses a significant challenge due to high user mobility that necessitates frequent triggering of beam switching procedures. Departing from reactive beam switching based on the user device channel state feedback, proactive beam switching prepares in advance for upcoming beam switching decisions by exploiting accurate channel state information (CSI) prediction. In this paper, we develop a framework for autonomous self-trained CSI prediction for mmWave vehicular users where a base station (gNB) collects and labels a dataset that it uses for training recurrent neural network (RNN)-based CSI prediction model. The proposed framework exploits the CSI feedback from vehicular users combined with overhearing the C-V2X cooperative awareness messages (CAMs) they broadcast. We implement and evaluate the proposed framework using deepMIMO dataset generation environment and demonstrate its capability to provide accurate CSI prediction for 5G mmWave vehicular users. CSI prediction model is trained and its capability to provide accurate CSI predictions from various input features are investigated.

Keywords

Autonomous, Self-Trained Channel, State Prediction, Wave Vehicular Communications

Rsif Scholar Name

Abidemi Matthew Orimogunje

Rsif Scholar Nationality

Nigeria

Cohort

Cohort 4

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

University of Rwanda (UR), Rwanda

Funding Statement

This work was jointly supported by the African Center of Excellence in Internet of Things (ACEIoT) from College of Science and Technology, University of Rwanda, and The Regional Scholarship and Innovation Fund (RSIF). In addition, this work received funding from the Horizon 2020 research and innovation 

Recommended Citation

Orimogunje, A., Ninkovic, V., Twahirwa, E., Gashema, G., & Vukobratovic, D. (2026). Autonomous Self-Trained Channel State PredictionMethod for mmWave Vehicular Communications. Electrical Engineering and Systems Science https://doi.org/10.48550/arXiv.2410.02326