Staff publications (AIRS)

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Browsing Staff publications (AIRS) by Author "Al-Rubaye, Saba"

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    Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization
    (IEEE, 2024-12-08) Li, Jiawei; Wang, Dawei; Zhang, Jiankang; Alfarraj, Osama; He, Yixin; Al-Rubaye, Saba; Yu, Keping; Mumtaz, Shahid
    This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.
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    Mitigating no fault found phenomena through ensemble learning: a mixture of experts approach
    (IEEE, 2024-09-24) Liu, Zeyu; Kong, Xiangqi; Chen, Yang; Wang, Ziyue; Jia, Huamin; Al-Rubaye, Saba
    In the aviation industry, the reliance on precise fault diagnostic decision-making is critical for equipment maintenance. A significant challenge encountered is the erroneous categorization of components under 'No Fault Found' (NFF), which subjects these components to unwarranted repairs or further testing. Such misclassifications not only trap on airlines through costly cycles of unnecessary maintenance but also exacerbate degeneration and potential safety hazards. Consequently, there is a heightened demand for the development of effective fault diagnosis models that are adapting to the aircraft complex systems and adeptly addressing issues related to the NFF phenomenon. In this study, we draw inspiration from ensemble learning and propose a multiple Naive Bayes experts (MNBMoEs) approach based on a mixture of experts (MoEs) model. This method leverages the predictive advantages of each sub-model on specific features, allowing the hybrid expert decision to outperform any single expert. It also includes a quantitative analysis method for the NFF issue, derived from the confusion matrix according to the industrial definition of NFF. Experiments evaluated on public datasets results show that the ensemble learning approach, based on Mixture of Multiple Naive-Bayes expert models, can effectively utilize the strengths of different models, improving fault diagnosis accuracy to 96.96%, with a maximum reduction in NFF occurrence rates of up to 94.17% and 84.2% model performance improvement.
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    Signal-to-interference-noise-ratio density distribution for UAV-carried IRS-to-6G ground communication
    (IEEE, 2025) Nnamani, Christantus Obinna; Anioke, Chidera Linda; Al-Rubaye, Saba; Tsourdos, Antonios
    This paper investigates the probability distribution of the signal-to-interference noise ratio (SINR) for a 6G communication system comprising a multi-antenna transmitter, an intelligent reflecting surface (IRS) and a remote receiver station. A common assumption in the literature is that the density distribution function for SINR and signal-to-noise ratio (SNR) of an IRS-to-ground communication follows a Rayleigh and Rician distribution. This assumption is essential as it influences the derivation of the properties of the communication system such as the physical layer security models and the designs of IRS controller units. Therefore, in this paper, we present an analytical derivation for the density distribution functions of the SINR for an IRS-to-6G ground communication ameliorating the typical assumptions in the literature. We demonstrated that the SINR density function of an IRS-to-6G ground communication contains a hypergeometric function. We further applied the derived density distribution function to determine the average secrecy rate for passive eavesdropping.
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    Sustainable 6G-NTN for seamless air mobility: exploring channel propagation characteristics
    (IEEE, 2025-04-08) Chen, Yang; Bocciarelli, Hugo; Al-Rubaye, Saba; Tsourdos, Antonios
    The air transportation vision for sustainable sixthgeneration (6 G) wireless communications networks revolves around ensuring ubiquitous coverage and spectral efficiency with enhanced network intelligence in the diverse communication scenarios. This vision extends beyond terrestrial networks to include non-terrestrial networks (NTN) by incorporating GX Inmarsat satellites and aircraft networks. In the context of 6G GX satellite scenarios, aircraft seamless transportation plays a crucial role as a densely populated intermediate network layer between ground networks and space-based ones. The paper proposes a new sustainable mechanism with mathematical model to improve channel propagation, which has been validated by crucial analysis of propagation channel modeling within the framework of 6 G technology. It highlights the significance of such modeling with the guarantee of dependable communications, maximizing availability, and establishing system parameters like antenna layout and relay deployment. It explores industry trends and ongoing field trial initiatives, offering valuable insights into the progress and outcomes that will shape the future of 6G NTN.