2 citations · 3 across the 5 of their papers we have counts for
8 papers
Model-Free Channel Estimation for Massive MIMO: A Channel Charting-Inspired Approach
Pinjun Zheng, Md. Jahangir Hossain, Anas Chaaban
Channel estimation is fundamental to wireless communications, yet it becomes increasingly challenging in massive multiple-input multiple-output (MIMO) systems where base stations e…
Deep Complex-valued Neural-Network Modeling and Optimization of Stacked Intelligent Surfaces
Abdullah Zayat, Omran Abbas, Loic Markley +1
We propose a complex-valued neural-network (CV-NN) framework to optimally configure stacked intelligent surfaces (SIS) in next-generation multi-antenna systems. Unlike conventional…
Mutli-Level Autoencoder: Deep Learning Based Channel Coding and Modulation
Ahmad Abdel-Qader, Anas Chaaban, Mohamed S. Shehata
In this paper, we design a deep learning-based convolutional autoencoder for channel coding and modulation. The objective is to develop an adaptive scheme capable of operating at v…
Tri-Hybrid Multi-User Precoding Based on Electromagnetically Reconfigurable Antennas
Pinjun Zheng, Yuchen Zhang, Tareq Y. Al-Naffouri +2
The tri-hybrid precoding architecture based on electromagnetically reconfigurable antennas (ERAs) is a promising solution for overcoming key limitations in multiple-input multiple-…
Enhanced Beampattern Synthesis Using Electromagnetically Reconfigurable Antennas
Pinjun Zheng, Md. Jahangir Hossain, Anas Chaaban
Beampattern synthesis seeks to optimize array weights to shape radiation patterns, playing a critical role in various wireless applications. In addition to theoretical advancements…
Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning
Mustafa Ghaleb, Mohanad Obeed, Muhamad Felemban +2
Federated Learning (FL) has emerged as a significant paradigm for training machine learning models. This is due to its data-privacy-preserving property and its efficient exploitati…