Staff publications (AIRS)

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Browsing Staff publications (AIRS) by Subject "4006 Communications Engineering"

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    Explainable reinforcement and causal learning for improving trust to 6G stakeholders
    (IEEE, 2025-06-01) Arana-Catania, Miguel; Sonee, Amir; Khan, Abdul-Manan; Fatehi, Kavan; Tang, Yun; Jin, Bailu; Soligo, Anna; Boyle, David; Calinescu, Radu; Yadav, Poonam; Ahmadi, Hamed; Tsourdos, Antonios; Guo, Weisi; Russo, Alessandra
    Future telecommunications will increasingly integrate AI capabilities into network infrastructures to deliver seamless and harmonized services closer to end-users. However, this progress also raises significant trust and safety concerns. The machine learning systems orchestrating these advanced services will widely rely on deep reinforcement learning (DRL) to process multi-modal requirements datasets and make semantically modulated decisions, introducing three major challenges: (1) First, we acknowledge that most explainable AI research is stakeholder-agnostic while, in reality, the explanations must cater for diverse telecommunications stakeholders, including network service providers, legal authorities, and end users, each with unique goals and operational practices; (2) Second, DRL lacks prior models or established frameworks to guide the creation of meaningful long-term explanations of the agent's behaviour in a goal-oriented RL task, and we introduce state-of-the-art approaches such as reward machine and sub-goal automata that can be universally represented and easily manipulated by logic programs and verifiably learned by inductive logic programming of answer set programs; (3) Third, most explainability approaches focus on correlation rather than causation, and we emphasise that understanding causal learning can further enhance 6G network optimisation. Together, in our judgement they form crucial enabling technologies for trustworthy services in 6G. This review offers a timely resource for academic researchers and industry practitioners by highlighting the methodological advancements needed for explainable DRL (X-DRL) in 6G. It identifies key stakeholder groups, maps their needs to X-DRL solutions, and presents case studies showcasing practical applications. By identifying and analysing these challenges in the context of 6G case studies, this work aims to inform future research, transform industry practices, and highlight unresolved gaps in this rapidly evolving field.
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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.