Hamed Hojatian

Ph.D. in Electrical Engineering

Research Engineer @ Huawei

IEEE journals reviewer for JSAC, TWC, TCOM, WCL, CL, and TVT.


Intro

What I am all about.

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

(10/2024)

Published Patent

Ottawa, ON, CA

Our patent titled "Energy-efficient massive MIMO beamforming with machine learning optimization", developed during my internship at Ericsson, has been published. You can view it here.

(06/2024)

Accepted paper in journal IEEE TMLCN

Ottawa, ON, CA

Our paper " Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming " is accepted for publication in IEEE Transactions on Machine Learning in Communications and Networking.

(11/2023)

Joined Huawei Technologies

Ottawa, ON, CA

I joined Huawei Technologies Canada as a Research Engineer, working on cutting-edge projects in AI and 6G wireless communication.

(08/2022)

Accepted paper in IEEE GlobeCom 2022

Montreal, QC, CA

Our paper with title " Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming " is accepted for IEEE GlobeCom 2022.

(02/2022)

Accepted paper in IEEE Communications Letters

Montreal, QC, CA

The paper we submitted to IEEE Communications Letters has been accepted for publication. The simulation source code is avaible here.

(05/2021)

Accepted paper in IEEE Transaction on Wireless Communications

Montreal, QC, CA

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

My core strengths span wireless systems, AI/ML, and advanced signal processing.

Wireless Communications

Machine Learning & AI

Signal Processing

Algorithm & Protocol Design

Integrated Sensing & Communications

5G/6G PHY Layer

Education

2019 – 2023

Polytechnique Montréal affiliate with University dé Montréal (UdM)

Doctor of Philosophy

Montreal, QC, CA

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;

2014 – 2017

Isfahan University of Technology (IUT)

Master of Electrical and Computer Engineering

Isfahan, Iran

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.

2010 – 2014

Babol Noshirvani University of Technology (BNUT)

Supervisor:

Bachelor of Electrical and Computer Science

Babol, Iran

My thesis focused on the simulation of OFDM and OFDMA multi-user transmitters and receivers. Simulations were conducted in MATLAB.

EXPERIENCE

2023 – Now

Huawei

Research Engineer

Ottawa, ON, CA

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.

2022 – 2023

Ericsson

Research Intern

Montreal, QC, CA

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.

2018 – 2019

PolyMTL

Research Associate

Montreal, QC, CA

Prior to becoming a PhD researcher, I was a research associate in Dr. François Leduc-Primeau's lab.

2015 – 2018

Naghshe-Aval-Keyfiat (NAK) affiliate Mobile Company of Iran (MCI)

RF planner and optimizer

Tehran, Iran

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.

Publications

Journal Papers


"Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming"
H. Hojatian, J. Nadal, J-F. Frigon, F. Leduc-Primeau
IEEE Transactions on Machine Learning in Communications and Networking, 2024
[Paper]
"Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning"
H. Hojatian, J. Nadal, J-F. Frigon, F. Leduc-Primeau
IEEE Communications Letters, 2022
[Paper]
"Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming"
H. Hojatian, J. Nadal, J-F. Frigon, F. Leduc-Primeau
IEEE Transactions on Wireless Communications, 2021
[Paper]

Conference Papers


"SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming"
A. Hasanzadeh, H. Hojatian, J-F. Frigon, F. Leduc-Primeau
IEEE ICMLCN, 2024
[Paper]
"Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming"
H. Hojatian, J. Nadal, J-F. Frigon, F. Leduc-Primeau
IEEE GlobeCom, 2022
[Paper]
"RSSI-Based Hybrid Beamforming Design with Deep Learning"
H. Hojatian, Vu N. Ha, J. Nadal, J-F. Frigon, F. Leduc-Primeau
IEEE ICC, 2020
[Paper]
"Joint CFO and Channel Estimation in OFDM-based Massive MIMO Systems"
H. Hojatian, M.J. Omidi, H. Saeedi-Sourck, A. Farhang
IEEE IST, 2016
[Paper]

Patents


"ENERGY-EFFICIENT MASSIVE MIMO BEAMFORMING WITH MACHINE LEARNING OPTIMIZATION"
H. Hojatian, F. Leduc-Primeau, J. Nadal, J-F. Frigon
WO Patent 2024/201,108, 2024
[Patent]

Licenses & Certifications


IEEE GLOBECOM 2022

GLOBECOM2022

2022


4th IVADO/MILA school in Deep Learning

IVADO/MILA

2019


Deep Learning with Pytorch

Udemy

2019


LTE Optimization of Radio Netwrok (3GPP)

TECHCOM Consulting GmbH

2018


LTE Counter and KPI (Ericsson)

TECHCOM Consulting GmbH

2017


LTE Radio Network Planning

TECHCOM Consulting GmbH

2017

Skills

90%

Python  

90%

PyTorch

80%

MATLAB

90%

MS office  

30%

Java   Script  

80%

LaTeX

40%

HTML5    ,  CSS  

Reference