12 citations · 23 across the 8 of their papers we have counts for
8 papers
Topology-Preserving Deep Joint Source-Channel Coding for Semantic Communication
Omar Erak, Omar Alhussein, Fang Fang +1
Many wireless vision applications, such as autonomous driving, require preservation of global structural information rather than only per-pixel fidelity. However, existing Deep joi…
Adaptive Token Merging for Efficient Transformer Semantic Communication at the Edge
Omar Erak, Omar Alhussein, Hatem Abou-Zeid +2
Large-scale transformers are central to modern semantic communication, yet their high computational and communication costs hinder deployment on resource-constrained edge devices.…
Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication
Omar Erak, Omar Alhussein, Hatem Abou-Zeid +1
Large-scale transformer models have emerged as a powerful tool for semantic communication systems, enabling edge devices to extract rich representations for robust inference across…
TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Network
Nouf Alabbasi, Omar Erak, Omar Alhussein +3
The telecommunications industry's rapid evolution demands intelligent systems capable of managing complex networks and adapting to emerging technologies. While large language model…
Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication
Omar Erak, Omar Alhussein, Wen Tong
Task-oriented semantic communication systems have emerged as a promising approach to achieving efficient and intelligent data transmission in next-generation networks, where only i…
Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standards
Omar Erak, Nouf Alabbasi, Omar Alhussein +4
Recent studies show that large language models (LLMs) struggle with technical standards in telecommunications. We propose a fine-tuned retrieval-augmented generation (RAG) system b…
Large Language Model-Driven Curriculum Design for Mobile Networks
Omar Erak, Omar Alhussein, Shimaa Naser +3
This study introduces an innovative framework that employs large language models (LLMs) to automate the design and generation of curricula for reinforcement learning (RL). As mobil…
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback
Omar Erak, Hatem Abou-Zeid
The recent advances in machine learning and deep neural networks have made them attractive candidates for wireless communications functions such as channel estimation, decoding, an…