Huawei German Research Center

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multitenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.

Network slicing has been identified as the backbone of the rapidly evolving 5G technology. However, as its consolidation and standardization progress, there are no literatures that comprehensively discuss its key principles, enablers, and research challenges. This paper elaborates network slicing from an end-to-end perspective detailing its historical heritage, principal concepts, enabling technologies and solutions as well as the current standardization efforts. In particular, it overviews the diverse use cases and network requirements of network slicing, the pre-slicing era, considering RAN sharing as well as the end-to-end orchestration and management, encompassing the radio access, transport network and the core network. This paper also provides details of specific slicing solutions for each part of the 5G system. Finally, this paper identifies a number of open research challenges and provides recommendations toward potential solutions.

Resveratrol (3,4',5-trihydroxy-trans-stilbene) is a polyphenolic compound accounting to the stilbene class. Most stilbenes in plants act as antifungal phytoalexins, compounds that are usually synthesized only in response to infection or injury. Resveratrol has been detected in trees, in a few flowering plants, in peanuts, and in grapevines. The major dietary sources of resveratrol include grapes, wine, peanuts, and peanut products. Numerous in vitro studies describe different biological effects of resveratrol. The major impacts are the antioxidative, anti-inflammatory, and estrogenic effects as well as anticancer and chemopreventive activities. In order to reveal information on absorption, metabolism, and the consequent bioavailability of resveratrol, different research approaches were performed, including in vitro, ex vivo, and in vivo models, all of which are considered in this review. Summarizing the data, resveratrol is absorbed and metabolized. Around 75% of this polyphenol are excreted via feces and urine. The oral bioavailability of resveratrol is almost zero due to rapid and extensive metabolism and the consequent formation of various metabolites as resveratrol glucuronides and resveratrol sulfates. The potential biologic activity of resveratrol conjugates should be considered in future investigations.

Wireless vehicular communications and sensing technologies are key to enabling more advanced intelligent transportation systems (ITSs) with improved safety and efficiency. Within the realm of wireless communication, millimeter-wave (mmwave) technology has recently received much attention, providing rich spectrum resources to support the timely transmission of large amounts of data. This is especially important for vehicular applications because the number of sensors on modern vehicles is rapidly increasing and thus generating large amounts of data. To fully exploit this potential, understanding mm-wave vehicle-to-vehicle (V2V) propagation channels is crucial. In this article, we review the state of the art in mm-wave V2V channel measurements and modeling, describe recent directional V2V channel measurements performed in the 60-GHz band, and discuss future challenges to be addressed in mm-wave V2V channel measurements and modeling.

The purpose of this study was to investigate the implications of selected chemopreventive parameters and metabolic conversion of resveratrol in vivo. In two 8-week long feeding experiments with rats, a low-resveratrol diet containing 50 mg resveratrol per kg body weight (bw) and day and a high-resveratrol diet with 300 mg per kg bw and day were administered. For chemopreventive evaluation selected phase I and phase II enzymes of the biotransformation system, the total antioxidant activity, and the vitamin E status of the animals were determined. The level of resveratrol and its metabolites in the feces, urine, plasma, liver, and kidneys was identified and quantitated by high-performance liquid chromatography-diode array detection (HPLC-DAD) using synthesized resveratrol conjugate standards. Feeding of different dosages of resveratrol revealed no effect on the different chemopreventive parameters, except for the total antioxidant activity, which was elevated in plasma by 19% after feeding 50 mg resveratrol per kg bw and day. The formation of trans-resveratrol-3-sulfate, trans-resveratrol-4'-sulfate, trans-resveratrol-3,5-disulfate, trans-resveratrol-3,4'-disulfate, trans-resveratrol-3,4',5-trisulfate, trans-resveratrol-3-O-beta-D-glucuronide, and resveratrol aglycone was detected by HPLC analysis, depending on the biological material. Total resveratrol recovery in urine and feces of rats fed on 50 mg resveratrol per kg bw and day was 15% and 13%, respectively. For rats fed the higher dosage of 300 mg resveratrol per kg bw and day recovery was 54% and 17%, respectively. This is the first study performed with synthesized standards of relevant resveratrol conjugates. The lack of effect on the chemopreventive parameters is probably due to the formation of various resveratrol conjugates reducing its bioavailability in the rat.

The advanced fifth-generation (5G) infrastructure will not only be a sheer evolution of the current network generations but, more significantly, a revolution in the information and communication technology (ICT) field. The 5G technology will efficiently enable new secure, dependable, ultrareliable, and delay-critical services to everyone and everything, such as cognitive objects and cyberphysical systems (CPSs). A fully immersive experience and anything as a service are the primary drivers for a global adoption and market uptake of new technology components, beyond today's client-server model, where the network has been reduced to a mere pipe of bits. The network will become the nervous system of the true digital society and economy. This article gives fundamental insight into how the 5G mobile communications system is being designed to be powerful and, especially, flexible enough, thus meeting the foreseen and unknown traffic scenarios and services requirements. We also present how a massive adoption and exploitation of mobile-edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and services virtualization will make the 5G operating system (OS) feasible and business viable.

While there is clarity on the wide range of applications that are to be supported by 5G cellular communications, and standardization of 5G has now started in 3GPP, there is no conclusion yet on the detailed design of the overall 5G RAN. This article provides a comprehensive overview of the 5G RAN design guidelines, key design considerations, and functional innovations as identified and developed by key players in the field.1 It depicts the air interface landscape that is envisioned for 5G, and elaborates on how this will likely be harmonized and integrated into an overall 5G RAN, in the form of concrete control and user plane design considerations and architectural enablers for network slicing, supporting independent business-driven logical networks on a common infrastructure. The article also explains key functional design considerations for the 5G RAN, highlighting the difference to legacy systems such as LTE-A and the implications of the overall RAN design.

An aroma extract dilution analysis applied on an aroma distillate prepared from fresh apricots revealed (R)-gamma-decalactone, (E)-beta-damascenone, delta-decalactone, and (R/S)-linalool with the highest flavor dilution (FD) factors among the 26 odor-active compounds identified. On the basis of quantitative measurements performed by application of stable isotope dilution assays, followed by a calculation of odor activity values (OAVs), beta-ionone, (Z)-1,5-octadien-3-one, gamma-decalactone, (E,Z)-2,6-nonadienal, linalool, and acetaldehyde appeared with OAVs >100, whereas in particular certain lactones, often associated with an apricot aroma note, such as gamma-undecalactone, gamma-nonalactone, and delta-decalactone, showed very low OAVs (<5). An aroma recombinate prepared by mixing the 18 most important odorants in concentrations as they occurred in the fresh fruits showed an overall aroma very similar to that of apricots. Omission experiments indicated that previously unknown constituents of apricots, such as (E,Z)-2,6-nonadienal or (Z)-1,5-octadien-3-one, are key contributors to the apricot aroma.

Industrial Internet of Things (IIoT) is an emerging area that fifth-generation (5G) mobile communication system penetrates industrial manufacturing applications. The indoor factory has larger space and there are a lot of metal machine tools distributed in it, which makes its radio propagation characteristics and corresponding channel models significantly different from those of the indoor office and indoor hotspot. To support the design and the evaluation of the IIoT techniques, the 3rd Generation Partnership Project (3GPP) released the first 5G IIoT standard model in October 2019. In this article, we give a detailed explanation of this IIoT model and compare it with other indoor models. First, we introduce the standardization of 3GPP IIoT channel model and its motivation. Second, four IIoT subscenarios, which are classified according to the clutter density and antenna height, are described. Third, the potential frequency bands of 5G IIoT are summarized. Fourth, the models of channel parameters, including the path loss and the line-of-sight (LOS) probability, the root mean-square (RMS) delay spread, and the angular spread, are given. Among them, the models of path loss and LOS probability take the antenna height and clutter density into consideration. The model of RMS delay spread changes from frequency-dependent to volume-dependent in order to catch the size variation of factories. Finally, two newly added key channel characteristics, dual mobility and absolute time of arrival, which help to describe the robot movement and positioning, are also presented.

The detection of anomalies is an essential data mining task for achieving security and reliability in computer systems. Logs are a common and major data source for anomaly detection methods in almost every computer system. Recent studies have focused predominantly on one-class deep learning methods on manually specified log representations. The main limitation is that these models are not able to learn log representations describing the semantic differences between normal and anomaly logs, leading to a poor generalization on unseen logs. We propose Logsy, a classification-based method to learn log representations that allow to distinguish between normal system log data and anomaly samples from auxiliary log datasets, easily accessible via the internet. The idea behind such an approach to anomaly detection is that the auxiliary dataset is sufficiently informative to enhance the representation of the normal data, yet diverse to regularize against overfitting and improve generalization. We perform several experiments on publicly available datasets to evaluate the performance and properties, where we show improvement of 0.25 in F1 compared to previous methods.

Thermal processing of food results in the formation of various novel compounds, among others advanced glycation endproducts (AGEs). AGEs result from nonenzymatic glycation reactions between reducing sugars and free amino groups of proteins, peptides, or amino acids. Due to their potential noxious effects, alimentary AGEs are also called glycotoxins. This review provides a summary of the available evidence on the health effects of exaggerated intake of thermally treated food. Data from experimental studies in rodents and from clinical studies in healthy volunteers and in patients suffering from selected diseases in which AGEs are of pathogenetic importance (diabetes, chronic renal failure) are summarized. It is concluded that, an exaggerated intake of thermally processed foods may exert in vivo diabetogenic and nephrotoxic effects, induce low-grade inflammation, enhance oxidative stress, and promote atherosclerosis.

The concept of Artificial Intelligence for IT Operations (AIOps) combines big data and machine learning methods to replace a broad range of IT operations including availability and performance monitoring of services. Such platforms typically use separate models for each modality of monitoring data (e.g., textual properties and real-valued response time in logs and traces) to detect faults and upcoming anomalies in cloud services, which do not capture the existing correlation between the modalities. This paper extends the range of utilized data types for creation of a single model to improve the anomaly detection. We use a bimodal distributed tracing data from large cloud infrastructures in order to detect an anomaly in the execution of system components. We propose an anomaly detection method, which utilizes a single modality of the data with information about the trace structure. In the next step, we extend the single-modality neural architecture to a multimodal neural network with long short-term memory (LSTM) to enable the learning from the sequential nature of both modalities in the tracing data. Furthermore, we demonstrate an approach to detect dependent and concurrent events using the ability of the model to reconstruct the execution path. The implemented prototype is experimentally evaluated with data from a large-scale production cloud. The results demonstrate that the novel approaches outperform other deep-learning methods based on traditional architectures.

The article describes the potential gain by spectrum sharing between cellular operators in terms of network efficiency. The focus of the study is on a specific resource sharing scenario: spectrum sharing between two operators in cellular downlink transmission. If frequency bands are allocated dynamically and exclusively to one operator - a case called orthogonal spectrum sharing - significant gains in terms of achievable throughput (spectrum sharing gains between 50 percent and 100 percent) and user satisfaction are reported for asymmetric scenarios at link and system level as well as from two hardware demonstrators. Additionally, if frequency bands are allocated simultaneously to two operators - a case called non-orthogonal spectrum sharing - further gains are reported. In order to achieve these, different enablers from hardware technologies and base station capabilities are required. However, we argue that all requirements are fulfilled in 3GPP and newer mobile standards. Therefore, the results and conclusions of this overview article encourage to seriously consider the inter-operator spectrum sharing technologies.

The Mobile and wireless communications Enablers for the Twenty-twenty Information Society (METIS) project is laying the foundations of Fifth Generation (5G) mobile and wireless communication system putting together the point of view of vendors, operators, vertical players, and academia. METIS envisions a 5G system concept that efficiently integrates new applications developed in the METIS horizontal topics and evolved versions of existing services and systems. This article provides a first view on the METIS system concept, highlights the main features including architecture, and addresses the challenges while discussing perspectives for the further research work.

ABSTRACT In this paper, we describe an SDN‐based plastic architecture for 5G networks, designed to fulfill functional and performance requirements of new generation services and devices. The 5G logical architecture is presented in detail, and key procedures for dynamic control plane instantiation, device attachment, and service request and mobility management are specified. Key feature of the proposed architecture is flexibility , needed to support efficiently a heterogeneous set of services, including Machine Type Communication, Vehicle to X and Internet of Things traffic. These applications are imposing challenging targets, in terms of end‐to‐end latency, dependability, reliability and scalability. Additionally, backward compatibility with legacy systems is guaranteed by the proposed solution, and Control Plane and Data Plane are fully decoupled . The three levels of unified signaling unify Access, Non‐access and Management strata, and a clean‐slate forwarding layer , designed according to the software defined networking principle, replaces tunneling protocols for carrier grade mobility. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Scenarios envisaged for mobile communication systems beyond 2020 imply that future applications and services will impose highly diverse requirements on the system design. To appropriately respond to these requirements while ensuring an efficient usage of available spectrum and system resources, the

Modern society is increasingly moving toward complex and distributed computing systems. The increase in scale and complexity of these systems challenges O&amp;M teams that perform daily monitoring and repair operations, in contrast with the increasing demand for reliability and scalability of modern applications. For this reason, the study of automated and intelligent monitoring systems has recently sparked much interest across applied IT industry and academia. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to Machine Learning, AI, and Big Data. However, AIOps as a research topic is still largely unstructured and unexplored, due to missing conventions in categorizing contributions for their data requirements, target goals, and components. In this work, we focus on AIOps for Failure Management (FM), characterizing and describing 5 different categories and 14 subcategories of contributions, based on their time intervention window and the target problem being solved. We review 100 FM solutions, focusing on applicability requirements and the quantitative results achieved, to facilitate an effective application of AIOps solutions. Finally, we discuss current development problems in the areas covered by AIOps and delineate possible future trends for AI-based failure management.

Connected vehicle systems form the basis for future features of functions and applications within the automotive domain. In order to allow resource intensive services, cloud offloading and especially Mobile Edge Computing is a promising approach. In this paper, we present a detailed futuristic vehicular scenario -- Electronic Horizon -- and list the challenges. We argue that the resulting challenges are representative of many of the envisioned use-cases of Mobile Edge Computing. We then present how Information-Centric Networking in combination with Mobile Edge Computing has the potential to support such a futuristic scenario. Finally, we present research directions that could enhance the solution space.

Recent research has shown that both radio and visible light waves can be used to enable communications in highly dynamic vehicular environments. However, the roles of these two technologies and how they interact with each other in future vehicular communication systems remain unclear. Understanding the propagation characteristics is an essential step in investigating the benefits and shortcomings of each technology. To this end, we discuss salient properties of radio and visible light propagation channels, including radiation pattern, path loss modeling, noise and interference, and channel time variation. Comparison of these properties provides an important insight that the two communication channels can complement each other's capabilities in terms of coverage and reliability, thus better satisfying the diverse requirements of future cooperative intelligent transportation systems.

This work describes the architecture and prototype implementation of a novel trust-aware continuous authorization technology that targets consumer Internet of Things (IoT), e.g., Smart Home. Our approach extends previous authorization models in three complementary ways: (1) By incorporating trust-level evaluation formulae as conditions inside authorization rules and policies, while supporting the evaluation of such policies through the fusion of an Attribute-Based Access Control (ABAC) authorization policy engine with a Trust-Level-Evaluation-Engine (TLEE). (2) By introducing contextualized, continuous monitoring and re-evaluation of policies throughout the authorization life-cycle. That is, mutable attributes about subjects, resources and environment as well as trust levels that are continuously monitored while obtaining an authorization, throughout the duration of or after revoking an existing authorization. Whenever change is detected, the corresponding authorization rules, including both access control rules and trust level expressions, are re-evaluated. (3) By minimizing the computational and memory footprint and maximizing concurrency and modular evaluation to improve performance while preserving the continuity of monitoring. Finally we introduce an application of such model in Zero Trust Architecture (ZTA) for consumer IoT.