3,323 research outputs found

    Deep generative models for network data synthesis and monitoring

    Get PDF
    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

    Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

    Get PDF
    The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

    Get PDF
    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Quality of experience and access network traffic management of HTTP adaptive video streaming

    Get PDF
    The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming

    Design and Real-World Evaluation of Dependable Wireless Cyber-Physical Systems

    Get PDF
    The ongoing effort for an efficient, sustainable, and automated interaction between humans, machines, and our environment will make cyber-physical systems (CPS) an integral part of the industry and our daily lives. At their core, CPS integrate computing elements, communication networks, and physical processes that are monitored and controlled through sensors and actuators. New and innovative applications become possible by extending or replacing static and expensive cable-based communication infrastructures with wireless technology. The flexibility of wireless CPS is a key enabler for many envisioned scenarios, such as intelligent factories, smart farming, personalized healthcare systems, autonomous search and rescue, and smart cities. High dependability, efficiency, and adaptivity requirements complement the demand for wireless and low-cost solutions in such applications. For instance, industrial and medical systems should work reliably and predictably with performance guarantees, even if parts of the system fail. Because emerging CPS will feature mobile and battery-driven devices that can execute various tasks, the systems must also quickly adapt to frequently changing conditions. Moreover, as applications become ever more sophisticated, featuring compact embedded devices that are deployed densely and at scale, efficient designs are indispensable to achieve desired operational lifetimes and satisfy high bandwidth demands. Meeting these partly conflicting requirements, however, is challenging due to imperfections of wireless communication and resource constraints along several dimensions, for example, computing, memory, and power constraints of the devices. More precisely, frequent and correlated message losses paired with very limited bandwidth and varying delays for the message exchange significantly complicate the control design. In addition, since communication ranges are limited, messages must be relayed over multiple hops to cover larger distances, such as an entire factory. Although the resulting mesh networks are more robust against interference, efficient communication is a major challenge as wireless imperfections get amplified, and significant coordination effort is needed, especially if the networks are dynamic. CPS combine various research disciplines, which are often investigated in isolation, ignoring their complex interaction. However, to address this interaction and build trust in the proposed solutions, evaluating CPS using real physical systems and wireless networks paired with formal guarantees of a system’s end-to-end behavior is necessary. Existing works that take this step can only satisfy a few of the abovementioned requirements. Most notably, multi-hop communication has only been used to control slow physical processes while providing no guarantees. One of the reasons is that the current communication protocols are not suited for dynamic multi-hop networks. This thesis closes the gap between existing works and the diverse needs of emerging wireless CPS. The contributions address different research directions and are split into two parts. In the first part, we specifically address the shortcomings of existing communication protocols and make the following contributions to provide a solid networking foundation: • We present Mixer, a communication primitive for the reliable many-to-all message exchange in dynamic wireless multi-hop networks. Mixer runs on resource-constrained low-power embedded devices and combines synchronous transmissions and network coding for a highly scalable and topology-agnostic message exchange. As a result, it supports mobile nodes and can serve any possible traffic patterns, for example, to efficiently realize distributed control, as required by emerging CPS applications. • We present Butler, a lightweight and distributed synchronization mechanism with formally guaranteed correctness properties to improve the dependability of synchronous transmissions-based protocols. These protocols require precise time synchronization provided by a specific node. Upon failure of this node, the entire network cannot communicate. Butler removes this single point of failure by quickly synchronizing all nodes in the network without affecting the protocols’ performance. In the second part, we focus on the challenges of integrating communication and various control concepts using classical time-triggered and modern event-based approaches. Based on the design, implementation, and evaluation of the proposed solutions using real systems and networks, we make the following contributions, which in many ways push the boundaries of previous approaches: • We are the first to demonstrate and evaluate fast feedback control over low-power wireless multi-hop networks. Essential for this achievement is a novel co-design and integration of communication and control. Our wireless embedded platform tames the imperfections impairing control, for example, message loss and varying delays, and considers the resulting key properties in the control design. Furthermore, the careful orchestration of control and communication tasks enables real-time operation and makes our system amenable to an end-to-end analysis. Due to this, we can provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics. • We propose control-guided communication, a novel co-design for distributed self-triggered control over wireless multi-hop networks. Self-triggered control can save energy by transmitting data only when needed. However, there are no solutions that bring those savings to multi-hop networks and that can reallocate freed-up resources, for example, to other agents. Our control system informs the communication system of its transmission demands ahead of time so that communication resources can be allocated accordingly. Thus, we can transfer the energy savings from the control to the communication side and achieve an end-to-end benefit. • We present a novel co-design of distributed control and wireless communication that resolves overload situations in which the communication demand exceeds the available bandwidth. As systems scale up, featuring more agents and higher bandwidth demands, the available bandwidth will be quickly exceeded, resulting in overload. While event-triggered control and self-triggered control approaches reduce the communication demand on average, they cannot prevent that potentially all agents want to communicate simultaneously. We address this limitation by dynamically allocating the available bandwidth to the agents with the highest need. Thus, we can formally prove that our co-design guarantees closed-loop stability for physical systems with stochastic linear time-invariant dynamics.:Abstract Acknowledgements List of Abbreviations List of Figures List of Tables 1 Introduction 1.1 Motivation 1.2 Application Requirements 1.3 Challenges 1.4 State of the Art 1.5 Contributions and Road Map 2 Mixer: Efficient Many-to-All Broadcast in Dynamic Wireless Mesh Networks 2.1 Introduction 2.2 Overview 2.3 Design 2.4 Implementation 2.5 Evaluation 2.6 Discussion 2.7 Related Work 3 Butler: Increasing the Availability of Low-Power Wireless Communication Protocols 3.1 Introduction 3.2 Motivation and Background 3.3 Design 3.4 Analysis 3.5 Implementation 3.6 Evaluation 3.7 Related Work 4 Feedback Control Goes Wireless: Guaranteed Stability over Low-Power Multi-Hop Networks 4.1 Introduction 4.2 Related Work 4.3 Problem Setting and Approach 4.4 Wireless Embedded System Design 4.5 Control Design and Analysis 4.6 Experimental Evaluation 4.A Control Details 5 Control-Guided Communication: Efficient Resource Arbitration and Allocation in Multi-Hop Wireless Control Systems 5.1 Introduction 5.2 Problem Setting 5.3 Co-Design Approach 5.4 Wireless Communication System Design 5.5 Self-Triggered Control Design 5.6 Experimental Evaluation 6 Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload 6.1 Introduction 6.2 Problem and Related Work 6.3 Overview of Co-Design Approach 6.4 Predictive Triggering and Control System 6.5 Adaptive Communication System 6.6 Integration and Stability Analysis 6.7 Testbed Experiments 6.A Proof of Theorem 4 6.B Usage of the Network Bandwidth for Control 7 Conclusion and Outlook 7.1 Contributions 7.2 Future Directions Bibliography List of Publication

    One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation

    Full text link
    Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies distributed learning in wireless data center networks, which contain a central edge server and multiple edge workers to collaboratively train a shared global model and benefit from parallel computing. However, the distributed nature causes the vulnerability of the learning process to faults and adversarial attacks from Byzantine edge workers, as well as the severe communication and computation overhead induced by the periodical information exchange process. To achieve fast and reliable model aggregation in the presence of Byzantine attacks, we develop a signed stochastic gradient descent (SignSGD)-based Hierarchical Vote framework via over-the-air computation (AirComp), where one voting process is performed locally at the wireless edge by taking advantage of Bernoulli coding while the other is operated over-the-air at the central edge server by utilizing the waveform superposition property of the multiple-access channels. We comprehensively analyze the proposed framework on the impacts including Byzantine attacks and the wireless environment (channel fading and receiver noise), followed by characterizing the convergence behavior under non-convex settings. Simulation results validate our theoretical achievements and demonstrate the robustness of our proposed framework in the presence of Byzantine attacks and receiver noise.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl
    • …
    corecore