6 papers
Learning During Detection: Continual Learning for Neural OFDM Receivers via DMRS
Mohanad Obeed, Ming Jian
Deep neural networks (DNNs) have been increasingly explored for receiver design because they can handle complex environments without relying on explicit channel models. Nevertheles…
CoNet-Rx: Collaborative Neural Networks for OFDM Receivers
Mohanad Obeed, Ming Jian
Deep learning (DL) based methods for orthogonal frequency division multiplexing (OFDM) radio receivers demonstrated higher signal detection performance compared to the traditional…
Hybrid Neural/Traditional OFDM Receiver with Learnable Decider
Mohanad Obeed, Ming Jian
Deep learning (DL) methods have emerged as promising solutions for enhancing receiver performance in wireless orthogonal frequency-division multiplexing (OFDM) systems, offering si…
Joint Quantization and Pruning Neural Networks Approach: A Case Study on FSO Receivers
Mohanad Obeed, Ming Jian
Towards fast, hardware-efficient, and low-complexity receivers, we propose a compression-aware learning approach and examine it on free-space optical (FSO) receivers for turbulence…
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…
From Centralized RAN to Open RAN: A Survey on the Evolution of Distributed Antenna Systems
Mahmoud A. Hasabelnaby, Mohanad Obeed, Mohammed Saif +2
Next-generation mobile networks require evolved radio access network (RAN) architectures to meet the demands of high capacity, massive connectivity, reduced costs, and energy effic…