AYOOB SALARI

AYOOB

SALARI

Welcome to my website! My name is Ayoob, and I have developed extensive experience in electrical engineering, thanks to my education and work in the communication systems and electrical engineering fields.

I possess a passion for research and development and have an insatiable desire to learn and gain experience. I relish the opportunity to network with people and welcome any direct rebuke or advice that can help me grow in my profession.

profile

About Me

I have a Bachelor's degree from Iran University of Science and Technology and a Master's degree from Korea Advanced Institute of Science and Technology, both in Electrical Engineering. After completing my Master's degree, I worked as an engineer at FWUTech for over a year, gaining valuable field experience. I am currently a PhD candidate at the prestigious University of Sydney, where my research focuses on machine learning applications in 6G. I expect to finish my PhD by June 2023, which will further solidify my skills and expertise in this field.

  • Python
  • Matlab
  • HFSS
  • CST
  • OrCAD
  • SPICE
  • ThingsBoard
  • Node-Red

My Education

Electrical Engineering

2019-presentSydney - Australia

Centre of IoT and Telecommunication, School of Electrical and Information Engineering

The University of Sydney

Thesis: The Interplay between Computation and Communications

We devised new machine learning based algorithms that enhance the capacity of communication networks to support the increasing number of IoT devices and applications. We used advanced machine learning and non-orthogonal multiple access techniques to enable massive grant-free access where a large number of devices shared the same radio resources and the base station simultaneously identified and detected users, estimated their channels, and decoded their messages. This optimized the network capacity and radio resource allocation while respecting the service requirements of the target scenario. Then we presented federated learning as an alternate strategy for massive IoT situations due to privacy problems, limited power, and often insufficient transmission bandwidth. We proposed a new strategy in federated learning to counteract the impact of erroneous communication and investigated the effect of coding rate on the convergence of federated learning for both short packet and long packet communications when erroneous transmissions are taken into account.

Supervisor: Dr. Mahyar Shirvanimoghaddam

My Publications

    1. Design and Analysis of Clustering-Based Joint Channel Estimation and Signal Detection for NOMA

    A. Salari, M. Shirvanimoghaddam, B. Shahab, R. Arablouei, and S. Johnson

    Submitted to IEEE Transaction on Vehicular Technology - 2023

    2. Rate-Convergence Tradeoff of Federated Learning over Wireless Channel

    A. Salari, M. Shirvanimoghaddam, B. Vucetic and S. Johnson

    Submitted to IEEE Internet of Things Journal - 2023

    3. NOMA Joint Channel Estimation and Signal Detection Using Rotational Invariant Codes and GMM-Based Clustering

    A. Salari, M. Shirvanimoghaddam, B. Shahab, Y. Li, and S. Johnson

    IEEE Communications Letters - 2022

My Experiences

Get In Touch

Don't be shy about getting in touch with me if there's anything I can do.

Email Me