3 citations · 6 across the 11 of their papers we have counts for
11 papers
Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction
Aubida A. Al-Hameed, Mohammed M. H. Qazzaz, Maryam Hafeez +1
Proactive service assurance in O-RAN requires predicting per-slice SLA violations before they occur. The prediction model must be auditable by operators and must train across base…
xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks
Mohammed M. H. Qazzaz, Syed Ali Zaidi, Aubida A. Al-Hameed +2
This paper introduces a proactive Unmanned Aerial Vehicle (UAV) mobility management xApp for Open Radio Access Network (O-RAN) Near Real-Time Radio Intelligent Controller (Near-RT…
Agentic AI meets Neural Architecture Search: Proactive Traffic Prediction for AI-RAN
Abdelaziz Salama, Mohammed M. H. Qazzaz, Zeinab Nezami +2
Next-generation wireless networks require intelligent traffic prediction to enable autonomous resource management and handle diverse, dynamic service demands. The Open Radio Access…
Joint Optimisation of Load Balancing and Energy Efficiency for O-RAN Deployments
Mohammed M. H. Qazzaz, Abdelaziz Salama, Maryam Hafeez +1
Open Radio Access Network (O-RAN) architecture provides an intrinsic capability to exploit key performance monitoring (KPM) within Radio Intelligence Controller (RIC) to derive net…
Semantic-Aware Edge Intelligence for UAV Handover in 6G Networks
Aubida A. Al-Hameed, Mohammed M. H. Qazzaz, Maryam Hafeez +1
6G wireless networks aim to exploit semantic awareness to optimize radio resources. By optimizing the transmission through the lens of the desired goal, the energy consumption of t…
Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN
Abdelaziz Salama, Zeinab Nezami, Mohammed M. H. Qazzaz +2
The deployment of AI agents within legacy Radio Access Network (RAN) infrastructure poses significant safety and reliability challenges for future 6G networks. This paper presents…
EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Abdelaziz Salama, Mohammed M. H. Qazzaz, Syed Danial Ali Shah +3
Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges,…
FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers
Abdelaziz Salama, Mohammed M. H. Qazzaz, Syed Danial Ali Shah +2
This work proposes an integrated approach for optimising Federated Learning (FL) communication in dynamic and heterogeneous network environments. Leveraging the modular flexibility…
Optimizing Search and Rescue UAV Connectivity in Challenging Terrain through Multi Q-Learning
Mohammed M. H. Qazzaz, Syed A. R. Zaidi, Desmond C. McLernon +2
Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a…
Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networks
Mohammed M. H. Qazzaz, Łukasz Kułacz, Adrian Kliks +3
The disaggregated, distributed and virtualised implementation of radio access networks allows for dynamic resource allocation. These attributes can be realised by virtue of the Ope…
FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA
Abdelaziz Salama, Syed Ali Zaidi, Des McLernon +1
Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the ra…