abdulkarimgizzini/Deep-Learning-Based-Channel-Estimation-Schemes-for-IEEE-802.11p-Standard
This repository includes the source code of the STA-DNN and TRFI DNN channel estimators proposed in "Deep Learning Based Channel Estimation Schemes for IEEE 802.11 p Standard" and "Joint TRFI and Deep Learning for Vehicular Channel Estimation" papers that are published in the IEEE Access journal and the proceedings of the 2020 IEEE GLOBECOM Workshops, respectively.
This project helps researchers and engineers working with vehicular communication systems, specifically those using the IEEE 802.11p standard. It takes raw simulation data representing wireless channel conditions and produces enhanced estimates of these channels using deep learning techniques. The primary users are wireless communication researchers and developers focused on improving the reliability of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications.
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Use this if you need to evaluate and compare advanced deep learning-based channel estimation methods for IEEE 802.11p vehicular communication scenarios.
Not ideal if you are looking for a plug-and-play solution for real-time vehicular communication, as this project focuses on research and simulation.
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Dec 12, 2023
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