7 papers
LLM-Aided A* Search in Non-Geometric Network Graphs
Nouf Alabbasi, Esraa Ghourab, Omar Alhussein
Finding the shortest path in non-geometric network graphs, where edge weights encode arbitrary metrics such as latency or monetary cost rather than spatial distance, poses a challe…
LLM-Enabled NWDAF: A Step Toward AI-Native 6G Network Intelligence
Henok Daniel, Omar Alhussein, Cheng Li +2
The Network Data Analytics Function (NWDAF) is central to enabling zero-touch network management in fifth-generation (5G) networks by supporting real-time analytics and closed-loop…
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…
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.…
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…