Browsing by Author "Marchetti, Nicola"

Now showing 1 - 5 of 5
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    Customization and Trade-offs in 5G RAN Slicing
    Sexton, Conor; Marchetti, Nicola; DaSilva, Luiz A. (IEEE, 2019-04-01)
    The heterogeneity of the requirements for 5G necessitate a versatile 5G radio access network (RAN); slicing offers a way of realising a flexible RAN through customised virtual subnetworks. In this paper, we focus on how enabling lower layer flexibility in the RAN affects the development of RAN slicing, particularly in relation to ensuring isolation between RAN slices. We first examine how RAN slices may be individually tailored for different services. We follow this up with an examination of the potential time-frequency resource structure of the RAN, focusing on the trade-off between flexibility and the overhead related to ensuring coexistence between contrasting RAN slices. Based on this analysis, we suggest an approach that permits the allocation of resources to a service-type to be performed separately to resource allocation for individual services belonging to that type.
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    Dimensioning Spectrum to Support Ultra-reliable Low Latency Communication
    Gomes, Andre; Kibilda, Jacek; Marchetti, Nicola; DaSilva, Luiz A. (IEEE, 2023-03-01)
    Industry-led initiatives such as the Next G Alliance are currently considering how to dimension the spectrum required to support new classes of services envisioned beyond 5G. In particular, support for ultra-reliable low-latency communication (URLLC) brings the challenge of how to dimension stochastic wireless networks to meet stringent reliability and latency requirements. Our analysis indicates that the bandwidth needed to meet URLLC goals can be on the order of gigahertz, beyond what is available in today's mobile networks. Network densification can ease those bandwidth needs but requires new deployment strategies involving substantially larger numbers of sites. As an alternative, we consider multi-connectivity and multi-operator network sharing as efficient ways to reduce the demand for bandwidth without outright deployment of additional base stations.
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    Indoor Millimeter-Wave Systems: Design and Performance Evaluation
    Kibilda, Jacek; MacKenzie, Allen B.; Abdel-Rahman, Mohammad J.; Yoo, Seong Ki; Giordano, Lorenzo Galati; Cotton, Simon L.; Marchetti, Nicola; Saad, Walid; Scanlon, William G.; Garcia-Rodriguez, Adrian; Lopez-Perez, David; Claussen, Holger; DaSilva, Luiz A. (IEEE, 2020-06-01)
    Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. Although we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. This article introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts and then moves on to experimentally validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.
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    Resource Reservation in Sliced Networks: An Explainable Artificial Intelligence (XAI) Approach
    Barnard, Pieter; Macaluso, Irene; Marchetti, Nicola; DaSilva, Luiz A. (IEEE, 2022-05-16)
    The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-theart techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.
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    Robust Network Intrusion Detection Through Explainable Artificial Intelligence (XAI)
    Barnard, Pieter; Marchetti, Nicola; DaSilva, Luiz A. (IEEE, 2022-09)
    In this letter, we present a two-stage pipeline for robust network intrusion detection. First, we implement an extreme gradient boosting (XGBoost) model to perform supervised intrusion detection, and leverage the SHapley Additive exPlanation (SHAP) framework to devise explanations of our model. In the second stage, we use these explanations to train an auto-encoder to distinguish between previously seen and unseen attacks. Experiments conducted on the NSL-KDD dataset show that our solution is able to accurately detect new attacks encountered during testing, while its overall performance is comparable to numerous state-of-the-art works from the cybersecurity literature.