3 citations · 4 across the 14 of their papers we have counts for
14 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…
Two-Layer Reinforcement Learning-Assisted Joint Beamforming and Trajectory Optimization for Multi-UAV Downlink Communications
Ruiqi Wang, Essra M. Ghoura, Omar Alhussein +4
Unmanned aerial vehicles (UAVs) are pivotal for future 6G non-terrestrial networks, yet their high mobility creates a complex coupled optimization problem for beamforming and traje…
Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection
Mohammad Arif Rasyidi, Omar Alhussein, Sami Muhaidat +1
Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluat…
Hybrid Quantum-Classical Detection for RIS-Assisted SC-FDE via Grover Adaptive Search
Maryam Tariq, Omar Alhussein, Raneem Abdelraheem +3
Wideband and low-latency requirements in sixth-generation (6G) networks demand detectors that approach maximum-likelihood (ML) performance without incurring exponential complexity.…
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.…
Hybrid Quantum-Classical Maximum-Likelihood Detection via Grover-based Adaptive Search for RIS-assisted Broadband Wireless Systems
Maryam Tariq, Raneem Abdelrahim, Omar Alhussein +1
The escalating complexity and stringent performance demands of sixth-generation wireless systems necessitate advanced signal processing methods capable of simultaneously achieving…
Max-Min Fairness in Stacked Intelligent Metasurface-Aided Rate Splitting Networks
Abdullah Quran, Shimaa Naser, Maryam Tariq +2
This paper investigates a downlink multiuser multiple-input single-output system that integrates rate-splitting multiple access (RSMA) with a stacked intelligent metasurface (SIM)…
Balancing Subjectivity and Objectivity in Network Selection: A Decision-Making Framework Towards Digital Twins
Brahim Mefgouda, Hanen Idoudi, Mohammad Al-Quraan +4
Selecting the optimal radio access technology (RAT) during vertical handovers (VHO) in heterogeneous wireless networks (HWNs) is critical. Multi-attribute decision-making (MADM) is…
Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System
Yueqing Wang, Yikun Mei, Zhen Gao +4
The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA…
Electromagnetic Channel Modeling and Capacity Analysis for HMIMO Communications
Li Wei, Shuai S. A. Yuan, Chongwen Huang +6
Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves,…
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…
LPUF-AuthNet: A Lightweight PUF-Based IoT Authentication via Tandem Neural Networks and Split Learning
Brahim Mefgouda, Raviha Khan, Omar Alhussein +4
By 2025, the internet of things (IoT) is projected to connect over 75 billion devices globally, fundamentally altering how we interact with our environments in both urban and rural…
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…
Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation
Omar Alhussein, Ning Zhang, Sami Muhaidat +1
This paper explores the integration of active machine learning (ML) for 6G networks, an area that remains under-explored yet holds potential. Unlike passive ML systems, active ML c…