arXiv subjects

Hans D. Schotten

Publications and source records attributed to Hans D. Schotten.

At least 181 records · Page 10Linked to original sources

arXiv2020

Integration of 5G with TSN as Prerequisite for a Highly Flexible Future Industrial Automation: Time Synchronization based on IEEE 802.1AS

Industry 4.0 brings up new types of use cases, whereby mobile use cases play a significant role. These use cases have stringent requirements on both automation and communication systems that cannot be achieved with recent shop floor technologies. Therefore, novel technologies such as IEEE time-sensitive networking (TSN) and Open Platform Communications Unified Architecture (OPC UA) are being introduced. In addition, for the realization of mobile use cases, wireline technologies cannot be used and have to be replaced by wireless connections, which have to meet the high demands of the industrial landscape. Here, 5th generation wireless communication system (5G) is seen as a promising candidate. Especially encouraging and similarly challenging is the cooperative work of mobile robots, where particularly high demands on time synchronization arise. Therefore, this paper introduces a concept for the integration of TSN time synchronization (IEEE 802.1AS) conform with 5G to fulfill the requirements of these use cases. Furthermore, the paper describes a testbed for discrete manufacturing, consisting pre-dominantly of industrial equipment, in order to evaluate the presented approach.

cs.NI↗

arXiv2020

ARENA: A Data-driven Radio Access Networks Analysis of Football Events

Mass events represent one of the most challenging scenarios for mobile networks because, although their date and time are usually known in advance, the actual demand for resources is difficult to predict due to its dependency on many different factors. Based on data provided by a major European carrier during mass events in a football stadium comprising up to 30.000 people, 16 base station sectors and $1$Km$^2$ area, we performed a data-driven analysis of the radio access network infrastructure dynamics during such events. Given the insights obtained from the analysis, we developed ARENA, a model-free deep learning Radio Access Network (RAN) capacity forecasting solution that, taking as input past network monitoring data and events context information, provides guidance to mobile operators on the expected RAN capacity needed during a future event. Our results, validated against real events contained in the dataset, illustrate the effectiveness of our proposed solution.

cs.LG↗

arXiv2020

LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks

Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude of predefined requirements. In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides-latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vs-exploitation paradigm by means of a multi-armed-bandit-based(MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics. As opposed to traditional MAB methods that are blind to the underlying system, LACO relies on system structure information to expedite decisions. After a preliminary simulations campaign empirically proving the validness of our solution, we provide a robust implementation of LACO using off-the-shelf equipment to fully emulate realistic network conditions:near-optimal results within affordable computational time are measured when LACO is in place.

cs.NI↗

arXiv2020

Platoon--assisted Vehicular Cloud in VANET: Vision and Challenges

Intelligent connected vehicles equipped with wireless sensors, intelligent control system, and communication devices are expected to commercially launch and emerge on road in short-term. These smart vehicles are able to partially/fully drive themselves; collect data from sensors, make and execute decisions based on that data; communicate with other vehicles, pedestrians, and nodes installed on the road; and provide infotainment and value-added services, such as broadband transmission of ultra-high definition video, files/apps downloading and uploading, online gaming, access to social media, audio/video conference streaming (office-in-car), live TV streaming, etc.; and so on. In addition, it is also possible for autonomous vehicles to form a "platoon" on road; maintaining close proximity in order to reduce the consumption of fuel and/or emission of gas, decrease costs, increase safety, and enhance the efficiency of the legacy transportation system. These emerging vehicular applications demand a large amount of computing and communication capacity to excel in their compute-intensive and latency-sensitive tasks. Based on these facts, the authors of this paper presented a visionary concept -- "platoon-assisted vehicular cloud" -- that exploits underutilized resources in platoons to augment vehicular cloud aiming to provide cost-effective and on-demand computing resources. Moreover, the authors presented three potential scenarios and explained the exploitation of platoon resources and roadside infrastructure to facilitate new applications. Besides system design, the paper did also summarize a number of open research challenges with the purpose of motivating new advances and potential solutions to this field.

eess.SY↗

arXiv2020

Effect of Retransmissions on the Performance of C-V2X Communication for 5G

In recent years, the next generation of wireless communication (5G) plays a significant role in both industry and academy societies. Cellular Vehicle-to-Everything (C-V2X) communication technology has been one of the prominent services for 5G. For C-V2X transmission mode, there is a newly defined communication channel (sidelink) that can support direct C-V2X communication. Direct C-V2X communication is a technology that allows vehicles to communicate and share safety-related information with other traffic participates directly without going through the cellular network. The C-V2X data packet will be delivered to all traffic Users (UE) in the proximity of the Transmitter (Tx). Some UEs might not successfully receive the data packets during one transmission but the sidelink Tx is not able to check whether the Receivers (Rxs) get the information or not due to the lack of feedback channel. For enabling the strict requirements in terms of reliability and latency for C-V2X communication, we propose and evaluate one retransmission scheme and retransmission with different traffic speed scheme. These schemes try to improve the reliability of the safety-related data by one blind retransmission without requiring feedback. Although this retransmission scheme is essential to C-V2X communication, the scheme has a limitation in the performance aspect because of its redundant retransmission. Since radio resources for CV2X communication are limited, we have to detect the effect of retransmission on the performance of the communication system. To the end, the simulator for evaluating the proposed schemes for the C-V2X communication has been implemented, and the performances of the different communication schemes are shown through the Packet Reception Ratio (PRR).

cs.NI↗

arXiv2020

Multiservice-based Network Slicing Orchestration with Impatient Tenants

The combination of recent emerging technologies such as network function virtualization (NFV) and network programmability (SDN) gave birth to the novel Network Slicing paradigm. 5G networks consist of multi-tenant infrastructures capable of offering leased network "slices" to new customers (e.g., vertical industries) enabling a new telecom business model: Slice-as-a-Service (SlaaS). However, as the service demand gets increasingly dense, slice requests congestion may occur leading to undesired waiting periods. This may turn into impatient tenant behaviors that increase potential loss of the business attractiveness to customers. In this paper, we aim to i study the slicing admission control problem by means of a multi-queuing system for heterogeneous tenant requests, ii) derive its statistical behavior model, iii) find out the rational strategy of impatient tenants waiting in queue-based slice admission control systems, iv) prove mathematically and empirically the benefits of allowing infrastructure providers to share its information with the upcoming tenants, and v) provide a utility model for network slices admission optimization. Our results analyze the capability of the proposed SlaaS system to be approximately Markovian and evaluate its performance as compared to a baseline solution.

cs.NI↗

arXiv2020

Channel Estimation in C-V2X using Deep Learning

Channel estimation forms one of the central component in current OFDM systems that aims to eliminate the inter-symbol interference by calculating the CSI using the pilot symbols and interpolating them across the entire time-frequency grid. It is also one of the most researched field in the PHY with LS and MMSE being the two most used methods. In this work, we investigate the performance of deep neural network architecture based on CNN for channel estimation in vehicular environments used in 3GPP Rel.14 CV2X technology. To this end, we compare the performance of the proposed DL architectures to the legacy LS channel estimation currently employed in C-V2X. Initial investigations prove that the proposed DL architecture outperform the legacy CV2X channel estimation methods especially at high mobile speeds

eess.SP↗

arXiv2020

A Machine Learning Method for Prediction of Multipath Channels

In this paper, a machine learning method for predicting the evolution of a mobile communication channel based on a specific type of convolutional neural network is developed and evaluated in a simulated multipath transmission scenario. The simulation and channel estimation are designed to replicate real-world scenarios and common measurements supported by reference signals in modern cellular networks. The capability of the predictor meets the requirements that a deployment of the developed method in a radio resource scheduler of a base station poses. Possible applications of the method are discussed.

eess.SP↗

arXiv2020

Recursive Optimization of Finite Blocklength Allocation to Mitigate Age-of-Information Outage

As an emerging metric for the timeliness of information delivery, Age-of-Information (AoI) raises a special interest in the research area of tolerance-critical communications, wherein sufficiently short blocklength is usually adopted as an essential requirement. However, the interplay between AoI and finite blocklength is scantly treated. This paper studies the occurrence of high AoI, i.e., AoI outage, in TDMA systems with respect to the blocklength allocation among users. A Markov Decision Process model is set up for the problem, which enables a static state analysis, and therewith a policy iteration approach to improve the AoI robustness is proposed. The burstiness of outages is also analyzed to provide additional insights into this problem in the finite blocklength (FBL) regime. It is shown that, different from average AoI optimizations, a risk-sensitive approach is significantly beneficial for AoI outage optimizations, on account of the FBL regime.

cs.IT↗

arXiv2020

Robustness Analysis of Networked Control Systems with Aging Status

As an emerging metric of communication systems, Age of Information (AoI) has been derived to have a critical impact in networked control systems with unreliable information links. This work sets up a novel model of outage probability in a loosely constrained control system as a function of the feedback AoI, and conducts numerical simulations to validate the model.

cs.IT↗

arXiv2020

Machine Learning for Network Slicing Resource Management: A Comprehensive Survey

The emerging technology of multi-tenancy network slicing is considered as an essential feature of 5G cellular networks. It provides network slices as a new type of public cloud services, and therewith increases the service flexibility and enhances the network resource efficiency. Meanwhile, it raises new challenges of network resource management. A number of various methods have been proposed over the recent past years, in which machine learning and artificial intelligence techniques are widely deployed. In this article, we provide a survey to existing approaches of network slicing resource management, with a highlight on the roles played by machine learning in them.

cs.NI↗

arXiv2019

Rational Impatience Admission Control in 5G-sliced Networks: Shall I Bide my Slice Opportunity?

Recently, the specifications of the fifth generation (5G) of mobile networks have been released witnessing the industrial interest around the novel network slicing concept. This context is further enriched by the evolution of an emerging type of public cloud environment: the Slice-as-a-Service (SlaaS). Infrastructure providers or network operators deploy different admission strategy while processing network slice requests issued by infrastructure tenants based on service level agreements (SLAs) and current (and predicted) resource availability. However, when the service demand will reasonably get dense, congestions of slice requests may occur leading to long waiting periods. This may turn into impatient tenant behaviors that mitigate potential loss. This paper studies the rational strategies of impatient tenants waiting in queue-based slice admission control system, proving mathematically and empirically the benefits of allowing infrastructure provider to share its information with the upcoming tenants.

cs.NI↗

arXiv2019

Supervised Learning for Physical Layer based Message Authentication in URLLC scenarios

PHYSEC based message authentication can, as an alternative to conventional security schemes, be applied within \gls{urllc} scenarios in order to meet the requirement of secure user data transmissions in the sense of authenticity and integrity. In this work, we investigate the performance of supervised learning classifiers for discriminating legitimate transmitters from illegimate ones in such scenarios. We further present our methodology of data collection using \gls{sdr} platforms and the data processing pipeline including e.g. necessary preprocessing steps. Finally, the performance of the considered supervised learning schemes under different side conditions is presented.

eess.SP↗

arXiv2019

AI-assisted PHY technologies for 6G and beyond wireless networks

Machine Learning (ML) and Artificial Intelligence(AI) have become alternative approaches in wireless networksbeside conventional approaches such as model based solutionconcepts. Whereas traditional design concepts include the mod-elling of the behaviour of the underlying processes, AI basedapproaches allow to design network functions by learning frominput data which is supposed to get mapped to specific outputs(training). Additionally, new input/output relations can be learntduring the deployement phase of the function (online learning)and make AI based solutions flexible, in order to react to newsituations. Especially, new introduced use cases such as UltraReliable Low Latency Communication (URLLC) and MassiveMachine Type Communications (MMTC) in 5G make this ap-proach necessary, as the network complexity is further enhancedcompared to networks mainly designed for human driven traffic(4G, 5G xMBB). The focus of this paper is to illustrate exemplaryapplications of AI techniques at the Physical Layer (PHY) offuture wireless systems and therfore they can be seen as candidatetechnologies for e.g. 6G systems.

eess.SP↗

arXiv2019

Optimal Blocklength Allocation towards Reduced Age of Information in Wireless Sensor Networks

The freshness or timeliness of data at server is a significant key performance indicator of sensor networks, especially in tolerance critical applications such as factory automation. As an effective and intuitive measurement to data timeliness, the metric of Age of Information (AoI) has attracted an intensive recent interest of research. This paper initiates a study on the AoI of wireless sensor networks working in the finite blocklength (FBL) regime as a resource allocation problem, and proposes to minimize the long-term discounted system AoI as a Markov decision process (MDP). The proposed method with its optimum solved by Reinforced Learning technique is verified by simulations to outperform benchmarks, including the conventional error rate minimizing policy.

eess.SP↗

arXiv2019

D2D-Based Grouped Random Access to Mitigate Mobile Access Congestion in 5G Sensor Networks

The Fifth Generation (5G) wireless service of sensor networks involves significant challenges when dealing with the coordination of ever-increasing number of devices accessing shared resources. This has drawn major interest from the research community as many existing works focus on the radio access network congestion control to efficiently manage resources in the context of device-to-device (D2D) interaction in huge sensor networks. In this context, this paper pioneers a study on the impact of D2D link reliability in group-assisted random access protocols, by shedding the light on beneficial performance and potential limitations of approaches of this kind against tunable parameters such as group size, number of sensors and reliability of D2D links. Additionally, we leverage on the association with a Geolocation Database (GDB) capability to assist the grouping decisions by drawing parallels with recent regulatory-driven initiatives around GDBs and arguing benefits of the suggested proposal. Finally, the proposed method is approved to significantly reduce the delay over random access channels, by means of an exhaustive simulation campaign.

cs.NI↗

arXiv2019

Methodologies of Link-Level Simulator and System-Level Simulator for C-V2X Communication

At the time of the development, standardization, and further improvement are vital to the modern cellular systems such as the next generation wireless communication (5G). Simulations are essential to test and optimize algorithms and procedures prior to their implementation process of the equipment manufactures. In order to evaluate system performance at different levels, accurate simulations of simple setups, as well as simulations of more complex systems via abstracted models are necessary. In this work, two new simulators for the sidelink Cooperative-Vehicle-to-Everything (C-V2X) communication have been implemented and carried out on both the physical layer (Link-Level (LL)) and network layer (System-Level (SL)). Detailed methodologies of the LL and SL simulators for C-V2X communication have been illustrated. In the LL simulator, we get the mapping curves of BLER and Signal-to-Noise-Ratio (SNR), which are used as a baseline for measuring the performance of the LL simulation. In addition, these mapping curves are used as the important Link-to-System (L2S) interfaces. The SL simulator is utilized for measuring the performance of cell networking and simulating large networks comprising of multiple eNBs and UEs. Finally, the simulation results of both simulators for CV2X communication are presented, which shows that different objectives can be met by using LL or SL simulations types.

eess.SP↗

arXiv2019

Two Decades of SCADA Exploitation: A Brief History

Since the early 1960, industrial process control has been applied by electric systems. In the mid 1970's, the term SCADA emerged, describing the automated control and data acquisition. Since most industrial and automation networks were physically isolated, security was not an issue. This changed, when in the early 2000's industrial networks were opened to the public internet. The reasons were manifold. Increased interconnectivity led to more productivity, simplicity and ease of use. It decreased the configuration overhead and downtimes for system adjustments. However, it also led to an abundance of new attack vectors. In recent time, there has been a remarkable amount of attacks on industrial companies and infrastructures. In this paper, known attacks on industrial systems are analysed. This is done by investigating the exploits that are available on public sources. The different types of attacks and their points of entry are reviewed in this paper. Trends in exploitation as well as targeted attack campaigns against industrial enterprises are introduced.

cs.CR↗