CV


FA
Mojtaba Hajiabadi

Mojtaba Hajiabadi

Assistant Professor

Faculty: Electrical and Computer Engineering

Department: Electronic

Degree: Ph.D

CV
FA
Mojtaba Hajiabadi

Assistant Professor Mojtaba Hajiabadi

Faculty: Electrical and Computer Engineering - Department: Electronic Degree: Ph.D |

Dr. Mojtaba Hajiabadi, a graduate of Electrical Engineering with a focus on Telecommunication Systems, earned his Bachelor's degree in 2012 from the University of Birjand, his Master's degree in 2014, and his Ph.D. in 2018 from Ferdowsi University of Mashhad. He was an outstanding student, holding third rank in his Bachelor's, first in his Master's, and first in his Ph.D. In 2018, he completed a doctoral research opportunity at KU Leuven University in Belgium under the supervision of Professor Marc Moonen, a distinguished IEEE Fellow.

During his Ph.D., Dr. Hajiabadi received several awards from the National Elites Foundation, including research grants, teaching assistantships, and an international research opportunity grant. After completing his studies, he worked for five years in Iran's telecommunications industry, contributing to satellite communication transceivers in the aerospace sector and optical telecommunication systems at the Infrastructure Communications Company. Since 2022, he has been a full-time faculty member in the Communication Engineering Department, Faculty of Electrical and Computer Engineering, at the University of Birjand. His research interests include adaptive filters, machine learning, and AI in wireless communication systems.

If you are interested in collaborating on research in his field of expertise, you may contact him via the following email address:

Email: mhajiabadi@birjand.ac.ir

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Enhancing NOMA User Rates in Smart Train Communication Systems via Joint RIS-IOS Deployment: Cascaded Channel Estimation and Phase Shift Design Under Impulsive Noise

AuthorsMojtaba Hajiabadi,Hamid Farrokhi,Masoud Ezzati
JournalWireless Personal Communications
Page number1-29
Serial number1
Volume number1
Paper TypeFull Paper
Published At2026
Journal GradeISI
Journal TypeElectronic
Journal CountryIran, Islamic Republic Of
Journal IndexJCR،Scopus
KeywordsReconfigurable intelligent surface (RIS), Intelligent omni surfaces (IOS), Correntropy, based stochastic gradient ascent (CSGA), Stochastic gradient descent (SGD), PD, NOMA

Abstract

In this paper, a novel framework is proposed to enhance the performance of power-domain non-orthogonal multiple access (PD-NOMA)-based communication systems in smart trains. The framework leverages the simultaneous deployment of reconfigurable intelligent surface (RIS) and intelligent omni surfaces (IOS) to improve users’ data rates. Due to the presence of impulsive noise arising from both internal and external electromagnetic sources within the train environment—and considering the high sensitivity of PD-NOMA systems to such disturbances—accurate estimation of the cascaded channel comprising both direct and reflected paths among the transmitter, RIS/IOS, and users is of critical importance. To address this, a correntropy-based stochastic gradient ascent (CSGA) algorithm is developed to provide robust channel estimation. Subsequently, a joint phase design strategy for the RIS and IOS is proposed, aiming to maximize the sum rate of PD-NOMA users. Simulation results demonstrate that the CSGA algorithm yields significantly higher channel estimation accuracy compared to the conventional stochastic gradient descent (SGD) approach. At SNR = 15 dB, CSGA algorithm reduces the mean square deviation (MSD) by approximately 12.2 dB and improves the sum-rate by about 2.59 b/s/Hz. The proposed framework, by employing joint phase optimization of RIS and IOS, exhibits strong resilience to impulsive noise and outperforms conventional architectures.

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