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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
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. Available
Hojatian, H. (2023). Beamforming Design for Massive MIMO Systems with Deep Neural Networks [Ph.D. thesis, Polytechnique Montréal]. Available
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. External link
Hojatian, H., Nadal, J., Frigon, J.-F., & Leduc-Primeau, F. (2022, December). Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming [Paper]. IEEE Global Communications Conference (GLOBECOM 2022), Rio de Janeiro, Brazil. External link
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. External link
Hojatian, H., Ha, V. N., Nadal, J., Frigon, J.-F., & Leduc-Primeau, F. (2020, June). RSSI-Based Hybrid Beamforming Design with Deep Learning [Paper]. IEEE International Conference on Communications (ICC 2020), Dublin, Ireland (6 pages). External link
Karkan, A. H., Hojatian, H., Frigon, J.-F., & Leduc-Primeau, F. (2024, May). SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming [Paper]. 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024), Stockhom, Sweden. External link
