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The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail stringent network requirements (e.g., ultra-low deterministic latency), which hinders network operators in managing their resources efficiently. In this article, we introduce the network digital twin (NDT), a renovated concept of classical network modeling tools whose goal is to build accurate data-driven network models that can operate in real time. We describe the general architecture of the NDT and argue that modern machine learning (ML) technologies enable building some of its core components. Then we present a case study that leverages an ML-based NDT for network performance evaluation and apply it to routing optimization in a QoS-aware use case. Lastly, we describe some key open challenges and research opportunities yet to be explored to achieve effective deployment of NDTs in real-world networks. [ABSTRACT FROM AUTHOR]

Network slicing, as a key 5G enabling technology, is promising to support with more flexibility, agility, and intelligence towards the provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and large-dimensioned. This contradicts the dominant network slicing solutions that only customize immediate performance over one snapshot of the system in the literature. Instead, this paper first presents a two-stage slicing optimization model with time-averaged metrics to safeguard the network slicing in the dynamical networks, where prior environmental knowledge is absent but can be partially observed at runtime. Directly solving an off-line solution to this problem is intractable since the future system realizations are unknown before decisions. Therefore, we propose a learning augmented optimization approach with deep learning and Lyapunov stability theories. This enables the system to learn a safe slicing solution from both historical records and run-time observations. We prove that the proposed solution is always feasible and nearly optimal, up to a constant additive factor. Finally, we demonstrate up to $2.6\times $ improvement in the simulation when compared with three state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

As a key enabling technology for 5G network softwarization, network function virtualization (NFV) provides an efficient paradigm to optimize network resource utility for the benefits of both network providers and users. However, the inherent network dynamics and uncertainties from 5G infrastructure, resources, and applications are slowing down the further adoption of NFV in many emerging networking applications. Motivated by this, in this paper, we investigate the issues of network utility degradation when implementing NFV in dynamic networks, and design a proactive NFV solution from a fully stochastic perspective. Unlike existing deterministic NFV solutions, which assume given network capacities and/or static service quality demands, this paper explicitly integrates the knowledge of influential network variations into a two-stage stochastic resource utilization model. By exploiting the hierarchical decision structures in this problem, a distributed computing framework with two-level decomposition is designed to facilitate a distributed implementation of the proposed model in large-scale networks. The experimental results demonstrate that the proposed solution not only improves 3~5 folds of network performance, but also effectively reduces the risk of service quality violation. [ABSTRACT FROM AUTHOR]

• Waves in a granular chain are reconstructed from acoustic measurements. • A model is developed based on the acoustic impulse response of one bead. • Acoustic inversion is performed using exact and perturbed acoustic pressures. • Effects of standoff distance and choice of regularization method are examined. • Reasonably accurate reconstruction is predicted from numerical simulations. This paper presents a method for reconstructing transient waves traveling in a discrete granular chain from acoustic pressure measurements made in the resulting sound field. A transient vibro-acoustic model is developed, based on the acoustic impulse response of an accelerating sphere, which superposes acoustic radiation from each bead of a granular chain using a convolution integral. We assume straight-line radiation of the acoustic pressure and constant position of the granular chain during propagation of a primary traveling wave. The continuous convolution integral is discretized to obtain the transfer matrix of the acoustic field response that correlates to vibrational motion of a discrete granular chain. To overcome ill-posedness in inverse processing, Tikhonov regularization with appropriate parameter-choice methods is applied. Acoustic field pressures are sampled with a linear microphone array conformal to the geometry of a granular chain at various standoff distances. It is found that the amplitude and speed of the primary traveling wave in a discrete granular chain can be reconstructed from acoustic field responses. The accuracy of reconstruction and the parameter sensitivity are examined with finite element analysis results as benchmarks. For acoustic field pressures contaminated by white noise, root-mean-square error analysis demonstrates that reasonably good reconstruction accuracy can still be achieved when the ill-conditioning of the convolution matrix and the matching of acoustic field pressures are balanced well. This study provides a useful approach to measure and visualize pulse transmission through a discrete granular medium in a time-efficient and cost-effective manner. [ABSTRACT FROM AUTHOR]

The dynamic characteristics of electric drive systems are crucial in electric vehicles. Based on the dynamic finite element method and previous studies, this study proposes and analyzes a new mathematical model for a motor longitudinally mounted on a centralized electric drive system of a pure electric vehicle. First, we analyze the largest torque ripple of a fractional slot concentrated winding inner-mounted permanent magnet synchronous motor designed for commercial electric vehicles. This torque ripple is identified as one of the excitations influencing the dynamic performance of the electric drive system. Second, a new dynamic mathematical model for the electric drive system is established. Third, we investigate the linear vibration responses of the system subject to torque ripple and transmission error. Finally, the relationships between critical motor parameters and dynamic mesh force are revealed. The results demonstrate that the proposed theoretical method can effectively determine the dynamic characteristics of the electric drive system, thereby providing valuable theoretical guidance for the design and optimization of the motor and electric drive system.