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Showing 2 results for Forouzan
Design and Optimizing of a GaN HEMT Power Amplifier Based on the Inclined Planes System Optimization Algorithm for Wireless Applications
M. Soruri, S. M. Razavi, M. Forouzanfar,
Volume 18, Issue 3 (September 2022)
Abstract
Power amplifier is one of the main components in the RF transmitters. It must provide various stringent features that can lead to complicating the design. In this paper, a new optimizing method based on the inclined planes system optimization algorithm is presented for the design of a discrete power amplifier. It is evaluated in a 2.4-3 GHz power amplifier, which is designed based on “Cree’s CGH40010F GaN HEMT”. The optimization goals are input and output return losses, Power Added Efficiency, and Gain. Large signal simulation of the optimized power amplifier shows a good performance across the bandwidth. In this frequency range, the input and output return losses are about lower than -10 dB, the Power Added Efficiency is greater than 51%, while the Gain is higher than 13.5 dB. A two-tone test with a frequency space of 1 MHz is applied for the linearity evaluation of the designed power amplifier. The obtained result shows that the power amplifier has good linearity with a low memory effect.

Machine Learning-Driven Adaptive Modulation for VLC-Enabled Medical Body Sensor Networks
Reza Bayat Rizi, Amir R. Forouzan, Farshad Miramirkhani, Mohamad F. Sabahi,
Volume 20, Issue 4 (Special Issue on ADLEEE - December 2024)
Abstract
Visible Light Communication, a key optical wireless technology, offers reliable, high-bandwidth, and secure communication, making it a promising soloution for a variety of applications. Despite its many advantages, optical wireless communication faces challenges in medical environments due to fluctuating signal strength caused by patient movement. Smart transmitter structures can improve system performance by adjusting system parameters to the fluctuating channel conditions. The purpose of this research is to examine how adaptive modulation performs in a medical body sensor network system that uses visible light communication. The analysis focuses on various medical situations and investigates machine learning algorithms. The study compares adaptive modulation based on supervised learning with that based on reinforcement learning. The findings indicate that both approaches greatly improve spectral efficiency, emphasizing the significance of implementing link adaptation in visible light communication-based medical body sensor networks. The use of the Q-learning algorithm in adaptive modulation enables real-time training and enables the system to adjust to the changing environment without any prior knowledge about the environment. A remarkable improvement is observed for photodetectors on the shoulder and wrist since they experience more DC gain.
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Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.