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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.

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.

Graph Neural Networks (GNN) have recently exploded in the Machine Learning area as a novel technique for modeling graph-structured data. This makes them especially suitable for applications in the networking field, as communication networks inherently comprise graphs at many levels (e.g., topology, routing, user connections). In this demo, we will present IGNNITION, an open-source framework for fast prototyping of GNNs applied to communication networks. This framework is especially designed for network engineers and/or researchers with limited background on neural network programming. IGNNITION comprises a set of tools and functionalities that eases and accelerates the whole implementation process, from the design of a GNN model, to its training, evaluation, debugging, and integration into larger network applications. In the demo, we will show how a user can implement a complex GNN model applied to network performance modeling (RouteNet), following three simple steps. This open-source project has received funding from the European Union’s Horizon 2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No. 871528. This article reflects only the authors’ view; the EC is not responsible for any use that may be made of the information it contains. The work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA).

Border Gateway Protocol (BGP) is the standard inter-domain routing protocol that is used to exchange reachability information among Wide Area Networks (WANs). BGP is a policy-based routing protocol that introduces a lot of flexibility. However, this flexibility increases the configuration complexity. In this research, we introduce DeepBGP as a neural network-based system that synthesizes network configuration given a high-level operator intent. We adopt Graph Neural Network (GNN) to represent network topology and generate partial network configuration. A validation unit is then used to calculate a reward based on which an Evolution Strategies (ES) optimizer updates neural network parameters. Since ES does not require backpropagation, they provide a significant reduction in calculation time. Further, the recent advances in deep learning with strong hardware acceleration and the parallelization capabilities offered by ES provide great potential in scaling the proposed solution to larger topologies. We demonstrate experimentally that DeepBGP can generate a network-wide configuration for both Huawei and Cisco devices while fulfilling operator requirements. We also show how Deep-BGP scales when the network size increases, and how hardware acceleration could improve the scalability of the system.

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.

The free and forced vibrations of a linear string with a local spring-damper on a partial elastic foundation, as well as a linear string on a viscoelastic foundation conceptualized as a continuous distribution of springs and dampers, are studied in this paper. Exact, analytical results are obtained for the free and forced response to a harmonic excitation applied at one end of the string. Relations between mode complexity and energy confinement with the dispersion in the string system are examined for the steady-state forced vibration, and numerical methods are applied to simulate the transient evolution of energy propagation. Eigenvalue loci veering and normal mode localization are observed for weakly coupled subsystems, when the foundation stiffness is sufficiently large, for both the spatially symmetric and asymmetric systems. The forced vibration results show that nonproportional damping-induced mode complexity, for which there are co-existing regions of purely traveling waves and standing waves, is attainable for the dispersive string system. However, this wave transition phenomenon depends strongly on the location of the attached discrete spring-damper relative to the foundation and whether the excitation frequency Ω is above or below the cutoff frequency ωc. When Ω

Previously, we have proposed a C/U-plane (Control/User plane) decoupled railway wireless network in which higher frequency bands are adopted by small cells to provide wider available spectra for the U-plane of passengers’ services. In order to guarantee reliable connectivity to wayside eNodeBs (eNBs), an onboard mobile relay (MR), consisting of two components, namely, MR-UE (User Equipment) and MR-AP (Access Point), is employed to forward passengers’ services over backhaul links between MR-UE and wayside eNBs. The remaining problem here is how to utilize spectra effectively and efficiently under this new configuration. Since a given wayside eNB hosts only one single accessed user most of the time, we design an additional uplink scheduler for the MR-UE to self-manage the usage of uplink resources, avoiding the complicated uplink grant procedure commonly used in the conventional cellular systems. Moreover, we develop eNB schedulers to coordinate the spectra in small cells. To deal with occasional multi-user scenarios, we propose an uplink scheduler switcher for the macro cell to judge which uplink scheduler should be activated. Furthermore, an uplink resource allocation scheme with high spectrum efficiency is deliberately designed for the new dual-scheduler configuration. Finally, we carry out theoretical analysis and numerical simulations to demonstrate the effectiveness of our proposed scheme.