Influence of Dataset Parameters on the Performance of Direct UE Positioning via Deep Learning

User equipment (UE) positioning accuracy is of paramount importance in current and future communications standard. However, traditional methods tend to perform poorly in non line of sight (NLoS) scenarios. As a result, deep learning is a candidate to enhance the UE positioning accuracy in NLoS environments. In this paper, we study the efficiency of deep learning on the 3GPP indoor factory (InF) statistical channel. More specifically, we analyse the impacts of several key elements on the positioning accuracy: the type of radio data used, the number of base stations (BS), the size of the training dataset, and the generalization ability of a trained model.Comment: Accepted for publication at the European Conference on Networks and Communications (EuCNC) 202

5G-CLARITY: 5G-Advanced Private Networks Integrating 5GNR, WiFi, and LiFi

The future of the manufacturing industry highly depends on digital systems that transform existing production and monitoring systems into autonomous systems fulfilling stringent requirements in terms of availability, reliability, security, low latency, and positioning with high accuracy. In order to meet such requirements, private 5G networks are considered as a key enabling technology. In this article, we introduce the 5G-CLARITY system that integrates 5GNR, WiFi, and LiFi access networks, and develops novel management enablers to operate 5G-Advanced private networks. We describe three core features of 5G-CLARITY, including a multi-connectivity framework, a high-precision positioning server, and a management system to orchestrate private network slices. These features are evaluated by means of packet-level simulations and an experimental testbed demonstrating the ability of 5G-CLARITY to police access network traffic, to achieve centimeter-level positioning accuracy, and to provision private network slices in less than one minuteThis work is supported by the European Commission’s Horizon 2020 research and innovation program under grant agreement No 871428, 5G-CLARITY project

The Potential Short- and Long-Term Disruptions and Transformative Impacts of 5G and Beyond Wireless Networks: Lessons Learnt from the Development of a 5G Testbed Environment

The capacity and coverage requirements for 5 th generation (5G) and beyond wireless connectivity will be significantly different from the predecessor networks. To meet these requirements, the anticipated deployment cost in the United Kingdom (UK) is predicted to be between £30bn and £50bn, whereas the current annual capital expenditure (CapEX) of the mobile network operators (MNOs) is £2.5bn. This prospect has vastly impacted and has become one of the major delaying factors for building the 5G physical infrastructure, whereas other areas of 5G are progressing at their speed. Due to the expensive and complicated nature of the network infrastructure and spectrum, the second-tier operators, widely known as mobile virtual network operators (MVNO), are entirely dependent on the MNOs. In this paper, an extensive study is conducted to explore the possibilities of reducing the 5G deployment cost and developing viable business models. In this regard, the potential of infrastructure, data, and spectrum sharing is thoroughly investigated. It is established that the use of existing public infrastructure (e.g., streetlights, telephone poles, etc.) has a potential to reduce the anticipated cost by about 40% to 60%. This paper also reviews the recent Ofcom initiatives to release location-based licenses of the 5G-compatible radio spectrum. Our study suggests that simplification of infrastructure and spectrum will encourage the exponential growth of scenario-specific cellular networks (e.g., private networks, community networks, micro-operators) and will potentially disrupt the current business models of telecommunication business stakeholders - specifically MNOs and TowerCos. Furthermore, the anticipated dense device connectivity in 5G will increase the resolution of traditional and non-traditional data availability significantly. This will encourage extensive data harvesting as a business opportunity and function within small and medium-sized enterprises (SMEs) as well as large social networks. Consequently, the rise of new infrastructures and spectrum stakeholders is anticipated. This will fuel the development of a 5G data exchange ecosystem where data transactions are deemed to be high-value business commodities. The privacy and security of such data, as well as definitions of the associated revenue models and ownership, are challenging areas - and these have yet to emerge and mature fully. In this direction, this paper proposes the development of a unified data hub with layered structured privacy and security along with blockchain and encrypted off-chain based ownership/royalty tracking. Also, a data economy-oriented business model is proposed. The study found that with the potential commodification of data and data transactions along with the low-cost physical infrastructure and spectrum, the 5G network will introduce significant disruption in the Telco business ecosystem

Digital twin for 5G and beyond

Although many countries have started the initial phase of rolling out 5G, it is still in its infancy with researchers from both academia and industry facing the challenges of developing to its full potential. With the support of Artificial Intelligence, development of digital transformation through the notion of a ‘Digital Twin’ has been taking off in many industries such as smart manufacturing, oil & gas, constructions, bio-engineering, and automotive. However, Digital Twins remain relatively new for 5G networks, despite the obvious potential in helping develop and deploy the complex 5G environment. This paper looks into these topics and discusses how Digital Twin could be a powerful tool to fulfil the potentials of 5G networks and beyond

Real-Time Performance of Industrial IoT Communication Technologies: A Review

With the growing need for automation and the ongoing merge of OT and IT, industrial networks have to transport a high amount of heterogeneous data with mixed criticality such as control traffic, sensor data, and configuration messages. Current advances in IT technologies furthermore enable a new set of automation scenarios under the roof of Industry 4.0 and IIoT where industrial networks now have to meet new requirements in flexibility and reliability. The necessary real-time guarantees will place significant demands on the networks. In this paper, we identify IIoT use cases and infer real-time requirements along several axes before bridging the gap between real-time network technologies and the identified scenarios. We review real-time networking technologies and present peer-reviewed works from the past 5 years for industrial environments. We investigate how these can be applied to controllers, systems, and embedded devices. Finally, we discuss open challenges for real-time communication technologies to enable the identified scenarios. The review shows academic interest in the field of real-time communication technologies but also highlights a lack of a fixed set of standards important for trust in safety and reliability, especially where wireless technologies are concerned.Comment: IEEE Internet of Things Journal 2023 | Journal article DOI: 10.1109/JIOT.2023.333250

Achieving Ultra-Reliable Low-Latency Communication (URLLC) in Next-Generation Cellular Networks with Programmable Data Planes

Recent advancements in wireless technologies towards the next-generation cellular networks have brought a new era that made it possible to apply cellular technology on traditionally-wired networks with tighter requirements, such as industrial networks. The next-generation cellular technologies (e.g., 5G and Beyond) introduce the concept of ultra-reliable low-latency communications (URLLC). This thesis presents a Software-Defined Networking (SDN) architecture with programmable data planes for the next-generation cellular networks to achieve URLLC. Our design deploys programmable switches between the cellular core and Radio Access Networks (RAN) to monitor and modify data traffic at the line speed. We introduce the concept of \textit{intra-cellular optimization}, a relaxation in cellular networks to allow pre-authorized in-network devices to communicate without being required to signal the core network. We also present a control structure, Unified Control Plane (UCP), containing a novel Ethernet Layer control protocol and an adapted version of link-state routing information distribution among the programmable switches. Our implementation uses P4 with an 5G implementation (Open5Gs) and a UE/RAN simulator. We implement a Python simulator to evaluate the performance of our system on multi-switch topologies by simulating the switch behavior. Our evaluation indicates latency reduction up to 2x with \textit{intra-cellular optimization} compared to the conventional architecture. We show that our design has a ten-millisecond level of control latency, and achieves fine-grained network security and monitoring.Comment: M.Sc. Thesis, Bogazici University, 202

Dissecting the impact of information and communication technologies on digital twins as a service

Recent advances on Edge computing, Network Function Virtualization (NFV) and 5G are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Digital Twin is a key piece in the industrial digital transformation and its benefits are very well studied in the literature. However, designing and implementing a Digital Twin system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. Therefore, prototyping the system is required to gradually validate and optimize Digital Twin solutions. In this work, an Edge Robotics Digital Twin system is implemented as a prototype that embodies the concept of Digital Twin as a Service (DTaaS). Such system enables real-time applications such as visualization and remote control, requiring low-latency and high reliability. The capability of the system to offer potential savings by means of computation offloading are analyzed in different deployment configurations. Moreover, the impact of different wireless channels (e.g., 5G, 4G and WiFi) to support the data exchange between a physical device and its virtual components are assessed within operational Digital Twins. Results show that potentially 16% of CPU and 34% of MEM savings can be achieved by virtualizing and offloading software components in the Edge. In addition, they show that 5G connectivity enables remote control of 20 ms, appearing as the most promising radio access technology to support the main requirements of Digital Twin systems.This work was supported in part by the H2020 European Union/Taiwan (EU/TW) Joint Action 5G-eDge Intelligence for Vertical Experimentation (DIVE) under Grant 859881, in part by the H2020 5Growth Project under Grant 856709, in part by the Madrid Government (Comunidad de Madrid-Spain) through the Multiannual Agreement with Universidad Carlos III de Madrid (UC3M) in the line of Excellence of University Professors under Grant EPUC3M21, and in part by the context of the V PRICIT (Regional Program of Research and Technological Innovation)

5G-CLARITY : 5G-advanced private networks integrating 5GNR, WiFi, and LiFi

The future of the manufacturing industry highly depends on digital systems that transform existing production and monitoring systems into autonomous systems fulfilling stringent requirements in terms of availability, reliability, security, low latency, and positioning with high accuracy. In order to meet such requirements, private 5G networks are considered as a key enabling technology. In this article, we introduce the 5G-CLARITY system that integrates 5GNR, WiFi, and LiFi access networks, and develops novel management enablers to operate 5G-Advanced private networks. We describe three core features of 5G-CLARITY, including a multi-connectivity framework, a high-precision positioning server, and a management system to orchestrate private network slices. These features are evaluated by means of packet-level simulations and an experimental testbed demonstrating the ability of 5G-CLARITY to police access network traffic, to achieve centimeter-level positioning accuracy, and to provision private network slices in less than one minute

Fall detection using single-tree complex wavelet transform

The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into "fall" and "ordinary activity" classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer. © 2012 Elsevier B.V. All rights reserved

5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0

This work has been partially funded by the H2020 project 5G-CLARITY (Grant No. 871428) and the Spanish national project TRUE-5G (PID2019-108713RB-C53).Fifth Generation (5G) is expected to meet stringent performance network requisites of the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G network slicing capabilities might not be enough in terms of degree of isolation for many private 5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network slicing, which refers to the use of dedicated and well isolated resources for each network slice at every network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of infrastructure slicing to provide isolation among production lines (PLs) in an industrial private 5G network. To that end, we develop a queuing theory-based model to estimate the end-to-end (E2E) mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic from PLs. Also we evaluate the use of Time-Sensitive Networking (TSN) against bare Ethernet to provide layer 2 connectivity among the 5G system components. We use a complete and realistic setup based on experimental and simulation data of the scenario considered. Our results support the effectiveness of infrastructure slicing to provide isolation in performance among the different slices. Then, using dedicated slices with segregated resources for each PL might reduce the number of the production downtimes and associated costs as the malfunctioning of a PL will not affect the network performance perceived by the performance-sensitive traffic from other PLs. Last, our results show that, besides the improvement in performance, TSN technology truly provides full isolation in the transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation, and bandwidth reservation capabilities.H2020 project 5G-CLARITY 871428Spanish Government PID2019-108713RB-C53TRUE-5