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.
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.
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
We considered the effect of variation of load resistance as a function of state of charge of the battery in the concept of magnetic wireless power transfer. We calculated the load resistance as a function of time and showed that the load resistance increased as the receiver acquired energy over time. We proposed a combination of topologies at the receiver that could harvest more energy compared to the currently available circuit models. To avoid performance degradation due to unmatched resonance frequencies between transmitter and receiver, we considered periodic feedback from the receiver. We demonstrated that there is a trade-off between the number of feedback that receiver sends and the received energy at the destination. We calculated the optimal feedback duration that maximized received energy for a point-to-point system model. We showed that the proposed protocol received higher energy compared to the existing point to point magnetic wireless power transfer system models.
Supervisor: Dr. Wan Choi
We demonstrated that the per-subcarrier DF relay technique is not optimal for obtaining the maximum achievable rate region in two-way OFDM relay channels, particularly when the direct link is insignificant. We proposed a multi-subcarrier DF relay technique that could achieve a larger rate region and developed an algorithm to determine the best allocation of resources for this approach. Based on our findings, we concluded that the suggested multi-subcarrier DF relay method was more suitable for the low to moderate SNR domain, while the AF strategy was more suitable for the high SNR regime, which might give advice for the design of practical two-way OFDM relay systems.
Supervisor: Dr. Razavi Zadeh
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