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Hojatian, H., Mlika, Z., Nadal, J., Frigon, J.-F., & Leduc-Primeau, F. (2024). Learning energy-efficient transmitter configurations for massive MIMO beamforming. IEEE Transactions on Machine Learning in Communications and Networking, 2, 939-955. Disponible
Hojatian, H., Mlika, Z., Nadal, J., Frigon, J.-F., & Leduc-Primeau, F. (2024). Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming. IEEE Transactions on Machine Learning in Communications and Networking, 2, 939-955. Lien externe
Karkan, A. H., Hojatian, H., Frigon, J.-F., & Leduc-Primeau, F. (mai 2024). SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming [Communication écrite]. 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024), Stockhom, Sweden. Lien externe
Hojatian, H. (2023). Beamforming Design for Massive MIMO Systems with Deep Neural Networks [Thèse de doctorat, Polytechnique Montréal]. Accès restreint
Hojatian, H., Nadal, J., Frigon, J.-F., & Leduc-Primeau, F. (2022). Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning. IEEE Communications Letters, 26(5), 1042-1046. Lien externe
Hojatian, H., Nadal, J., Frigon, J.-F., & Leduc-Primeau, F. (décembre 2022). Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming [Communication écrite]. IEEE Global Communications Conference (GLOBECOM 2022), Rio de Janeiro, Brazil. Lien externe
Hojatian, H., Nadal, J., Frigon, J.-F., & Leduc-Primeau, F. (2021). Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming. IEEE Transactions on Wireless Communications, 20(11), 7086-7099. Lien externe
Hojatian, H., Ha, V. N., Nadal, J., Frigon, J.-F., & Leduc-Primeau, F. (juin 2020). RSSI-Based Hybrid Beamforming Design with Deep Learning [Communication écrite]. IEEE International Conference on Communications (ICC 2020), Dublin, Ireland (6 pages). Lien externe
