Hamed Hojatian
Ph.D. in Electrical Engineering
Research Engineer @ Huawei
IEEE journals reviewer for JSAC, TWC, TCOM, WCL, CL, and TVT.
Intro
I am a research engineer at Huawei Advance Wireless Research LAB in Kanata, ON, CA. I obtained my B.Sc. degree in electrical engineering from the Babol Noshirvani University of Technology (BNUT), Babol, Iran, in 2014, and later earned an M.Sc. degree in wireless communication systems engineering from the Isfahan University of Technology (IUT), Isfahan, Iran, in 2017. In 2023, I successfully completed my Ph.D. with the Department of Electrical Engineering at Polytechnique Montréal, Montréal, QC, Canada. I have practical experience in 3G and LTE RAN planning and optimization. My current research pursuits encompass applied ML/AI in wireless communications, ISAC, massive MIMO communication systems, signal processing, and optimization.
News
Accepted paper in journal IEEE TMLCN
Our paper with title " Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming " is accepted for publication in IEEE Transactions on Machine Learning in Communications and Networking.
Accepted paper in IEEE GlobeCom 2022
Our paper with title " Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming " is accepted for IEEE GlobeCom 2022.
Accepted paper in IEEE Communications Letters
The paper we submitted to IEEE Communications Letters has been accepted for publication. The simulation source code is avaible here.
Accepted paper in IEEE Transaction on Wireless Communications
A paper that we submitted to the IEEE Transaction on Wireless Communications has been accepted for publication. The simulation source code is avaible here.
Expertise
Wireless communications
Deep learning
Signal processing
Massive MIMO
Integrated Sensing and Communications
Education
Polytechnique Montréal affiliate with University dé Montréal (UdM)
Doctor of Philosophy
Designing hybrid beamforming techniques for wireless networks, including an innovative RSSI-based hybrid beamforming design that leverages deep learning algorithms to enhance spectral and energy efficiency; Developing unsupervised learning techniques for intelligent beamforming and coordinated hybrid beam- forming in cell-free massive MIMO systems; Exploring subarray hybrid beamforming and efficient hardware design driven by unsupervised learning techniques, to reduce the complexity of wireless communication systems;
Isfahan University of Technology (IUT)
Master of Electrical and Computer Engineering
My master's thesis focused on the synchronization of time and frequency in a massive MIMO multiuser system uplink with frequency errors. Considering carrier frequency offset (CFO) and ICI in OFDM systems, I developed a method for jointly estimating the channel and CFO. My research has been extended to the design of a low-complexity receiver capable of compensating for CFOs and detecting symbols. As a result of my thesis, a paper was published in an IEEE conference.
Babol Noshirvani University of Technology (BNUT)
Supervisor:
Dr. Hossein Miare Naimi
Bachelor of Electrical and Computer Science
My thesis focused on the simulation of OFDM and OFDMA multi-user transmitters and receivers. Simulations were conducted in MATLAB.
EXPERIENCE
Huawei
Research Engineer
I am currently working as a Research Engineer at Huawei, focusing on the PHY Layer, where I apply AI/ML techniques to advance 6G technology.
Ericsson
Research Intern
Contributing to a project focused on enhancing energy efficiency within the Ericsson - Global Artificial Intelligence Accelerator (GAIA) framework. My role involved developing and implementing strategies to optimize energy consumption of massive MIMO system using ML/AI techniques. This collaborative effort with a multidisciplinary team resulted in significant achievements, including first inventor of a US patent and the publication of a journal paper.
Polytechnique Montréal affiliate University dé Montréal (UdM)
Research Associate
Prior to becoming a PhD researcher, I was a research associate in Dr. François Leduc-Primeau's lab.
Naghshe-Aval-Keyfiat (NAK) affiliate Mobile Company of Iran (MCI)
RF planner and optimizer
I began my career with the planning team, focusing on 3G and LTE. I became familiar with network optimization during my planning tasks. Due to my academic background and experience in network planning, I was able to join the Tehran project's network optimization team. In the beginning, I was a BSC owner, then I became a RNC owner of Tehran's network.
11th nathionwide Robotics competition
Vice-president
I was honored to be Vice-president of nationwide Robatics competition.
Publications
Journal Papers
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[1] H. Hojatian, J. Nadal, J-F. Frigon, and F. Leduc-Primeau,
"Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming", IEEE Transactions on Machine Learning in Communications and Networking, 2024.
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[2] H. Hojatian, J. Nadal, J-F. Frigon, and F. Leduc-Primeau,
"Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming", IEEE Transactions on Wireless Communications, 2022.
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[3] H. Hojatian, J. Nadal, J-F. Frigon, and F. Leduc-Primeau,
"Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning", IEEE Communications Letters, 2021.
Conference Papers
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[1] A.Hasanzadeh, H. Hojatian, J-F. Frigon, and F. Leduc-Primeau,
"SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming", IEEE ICMLCN 2024.
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[2] H. Hojatian, J. Nadal, J-F. Frigon, and F. Leduc-Primeau,
"Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming", IEEE GlobeCom 2022.
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[3] H. Hojatian, Vu N. Ha, J. Nadal, J-F. Frigon, and F. Leduc-Primeau,
"RSSI-Based Hybrid Beamforming Design with Deep Learning", IEEE ICC 2020.
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[4] H. Hojatian, MJ. Omidi, H. Saeedi-Sourck, A. Farhang,
"Joint CFO and Channel Estimation in OFDM-based Massive MIMO Systems", IEEE IST 2016.
Patents
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[1] H. Hojatian, F. Leduc-Primeau, J. Nadal, and J-F. Frigon,
"ENERGY-EFFICIENT MASSIVE MIMO BEAMFORMING WITH MACHINE LEARNING OPTIMIZATION", WO Patent 2024/201,108, 2024.