abdulkarimgizzini/DL-based-Channel-Estimation-in-Doubly-Dispersive-Environments-

This repository includes the source code of the DL-based symbol-by-symbol and frame-by-frame channel estimators proposed in "A Survey on Deep Learning Based Channel Estimation in Doubly Dispersive Environments" paper [1] that is published in the IEEE Access, 2022.

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This project offers deep learning methods to accurately estimate radio channel conditions, particularly in complex, rapidly changing environments like those in vehicular communications. It takes raw signal data, processes it through neural networks, and outputs improved channel estimates. Wireless communication researchers and engineers designing next-generation wireless systems will find this useful.

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Use this if you are a wireless researcher or engineer working on channel estimation in environments where both time and frequency dispersion are significant, such as high-speed mobile networks.

Not ideal if you need simple channel estimation for static or slowly changing wireless environments, or if you prefer traditional signal processing techniques over deep learning methods.

wireless-communication channel-estimation 5G-research vehicular-networks telecommunications-engineering
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MIT

Last pushed

Dec 12, 2023

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