1,213 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    Undergraduate Catalog of Studies, 2023-2024

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    A new global media order? : debates and policies on media and mass communication at UNESCO, 1960 to 1980

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    Defence date: 24 June 2019Examining Board: Professor Federico Romero, European University Institute (Supervisor); Professor Corinna Unger, European University Institute (Second Reader); Professor Iris Schröder, UniversitĂ€t Erfurt (External Advisor); Professor Sandrine Kott, UniversitĂ© de GenĂšveThe 1970s, a UNESCO report claimed, would be the “communication decade”. UNESCO had started research on new means of mass communication for development purposes in the 1960s. In the 1970s, the issue evolved into a debate on the so-called “New World Information and Communication Order” (NWICO) and the democratisation of global media. It led UNESCO itself into a major crisis in the 1980s. My project traces a dual trajectory that shaped this global debate on transnational media. The first follows communications from being seen as a tool and goal of national development in the 1960s, to communications seen as catalyst for recalibrated international political, cultural and economic relations. The second relates to the recurrent attempts, and eventual failure, of various actors to engage UNESCO as a platform to promote a new global order. I take UNESCO as an observation post to study national ambitions intersecting with internationalist claims to universality, changing understandings of the role of media in development and international affairs, and competing visions of world order. Looking at the modes of this debate, the project also sheds light on the evolving practices of internationalism. Located in the field of a new international history, this study relates to the recent rediscovery of the “new order”-discourses of the 1970s as well as to the increasingly diversified literature on internationalism. With its focus on international communications and attempts at regulating them, it also contributes to an international media history in the late twentieth century. The emphasis on the role of international organisations as well as on voices from the Global South will make contributions to our understanding of the historic macro-processes of decolonisation, globalisation and the Cold War

    Undergraduate Catalog of Studies, 2022-2023

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    Multi-objective resource optimization in space-aerial-ground-sea integrated networks

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    Space-air-ground-sea integrated (SAGSI) networks are envisioned to connect satellite, aerial, ground, and sea networks to provide connectivity everywhere and all the time in sixth-generation (6G) networks. However, the success of SAGSI networks is constrained by several challenges including resource optimization when the users have diverse requirements and applications. We present a comprehensive review of SAGSI networks from a resource optimization perspective. We discuss use case scenarios and possible applications of SAGSI networks. The resource optimization discussion considers the challenges associated with SAGSI networks. In our review, we categorized resource optimization techniques based on throughput and capacity maximization, delay minimization, energy consumption, task offloading, task scheduling, resource allocation or utilization, network operation cost, outage probability, and the average age of information, joint optimization (data rate difference, storage or caching, CPU cycle frequency), the overall performance of network and performance degradation, software-defined networking, and intelligent surveillance and relay communication. We then formulate a mathematical framework for maximizing energy efficiency, resource utilization, and user association. We optimize user association while satisfying the constraints of transmit power, data rate, and user association with priority. The binary decision variable is used to associate users with system resources. Since the decision variable is binary and constraints are linear, the formulated problem is a binary linear programming problem. Based on our formulated framework, we simulate and analyze the performance of three different algorithms (branch and bound algorithm, interior point method, and barrier simplex algorithm) and compare the results. Simulation results show that the branch and bound algorithm shows the best results, so this is our benchmark algorithm. The complexity of branch and bound increases exponentially as the number of users and stations increases in the SAGSI network. We got comparable results for the interior point method and barrier simplex algorithm to the benchmark algorithm with low complexity. Finally, we discuss future research directions and challenges of resource optimization in SAGSI networks

    Adaptive vehicular networking with Deep Learning

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    Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networks’ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities. In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects. The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup. The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios. The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the “ideal” optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark

    Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

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    Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks

    Integration of hybrid networks, AI, Ultra Massive-MIMO, THz frequency, and FBMC modulation toward 6g requirements : A Review

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    The fifth-generation (5G) wireless communications have been deployed in many countries with the following features: wireless networks at 20 Gbps as peak data rate, a latency of 1-ms, reliability of 99.999%, maximum mobility of 500 km/h, a bandwidth of 1-GHz, and a capacity of 106 up to Mbps/m2. Nonetheless, the rapid growth of applications, such as extended/virtual reality (XR/VR), online gaming, telemedicine, cloud computing, smart cities, the Internet of Everything (IoE), and others, demand lower latency, higher data rates, ubiquitous coverage, and better reliability. These higher requirements are the main problems that have challenged 5G while concurrently encouraging researchers and practitioners to introduce viable solutions. In this review paper, the sixth-generation (6G) technology could solve the 5G limitations, achieve higher requirements, and support future applications. The integration of multiple access techniques, terahertz (THz), visible light communications (VLC), ultra-massive multiple-input multiple-output ( ÎŒm -MIMO), hybrid networks, cell-free massive MIMO, and artificial intelligence (AI)/machine learning (ML) have been proposed for 6G. The main contributions of this paper are a comprehensive review of the 6G vision, KPIs (key performance indicators), and advanced potential technologies proposed with operation principles. Besides, this paper reviewed multiple access and modulation techniques, concentrating on Filter-Bank Multicarrier (FBMC) as a potential technology for 6G. This paper ends by discussing potential applications with challenges and lessons identified from prior studies to pave the path for future research
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