Discover @abdulkarimgizzini Open Source projects

Abdul Karim Gizzini (@abdulkarimgizzini)

abdulkarimgizzini

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Enhancing_Least_Square_Channel_Estimation_Using_Deep_Learning

This repository includes the source code of the LS-DNN based channel estimators proposed in "Enhancing Least Square Channel Estimation Using Deep Learning" paper that is published in the proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) virtual conference.

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.

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.

Temporal-Averaging-LSTM-based-Channel-Estimation-Scheme-for-IEEE-802.11p-Standard

This repository includes the source code of the LSTM-based channel estimators proposed in "Temporal Averaging LSTM-based Channel Estimation Scheme for IEEE 802.11 p Standard" paper that is published in the proceedings of the IEEE GLOBECOM 2022 conference that was held in Madrid (Spain).

CNN-Aided-Weighted-Interpolation-for-Channel-Estimation-in-Vehicular-Communications

This repository includes the source code of the CNN-based channel estimators proposed in "CNN Aided Weighted Interpolation for Channel Estimation in Vehicular Communications" paper [1] that is published in the IEEE Transactions on Vehicular Technology, 2021.

Low-Complex-Methods-for-Robust-Channel-Estimation-in-Doubly-Dispersive-Environments

This repository includes the source code of the DFT-based channel estimators proposed in "Low Complex Methods for Robust Channel Estimation in Doubly Dispersive Environments" paper [1] that is published in the IEEE Access, 2022.