Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the onboarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.Comment: 5 pages, 6 figures. Accepted for presentation at IEEE conference VTC2023-Spring. Available dataset at https://ieee-dataport.org/open-access/berlin-v2

AI-Driven, Predictive QoS for V2X Communications in 5G and beyond.

Με το ξεκίνημα της εποχής της συνδεδεμένης και αυτοματοποιημένης κινητικότητας με δυνατότητα 5G, έχουν προκύψει καινοτόμες υπηρεσίες Vehicle-to-Everything προς ασφαλέστερη και αυτοματοποιημένη οδήγηση. Οι απαιτήσεις που απορρέουν από αυτές τις υπηρεσίες θέτουν πολύ αυστηρές προκλήσεις στο δίκτυο κυρίως όσον αφορά την καθυστέρηση από άκρο σε άκρο και την αξιοπιστία των υπηρεσιών. Ταυτόχρονα, η τεχνητή νοημοσύνη εντός δικτύου που αναδύεται, αποκαλύπτει μια πληθώρα νέων δυνατοτήτων του δικτύου να ενεργεί με προληπτικό τρόπο προς την ικανοποίηση των προαναφερθέντων μεγάλων απαιτήσεων. Αυτή η διατριβή παρουσιάζει το PreQoS, έναν προγνωστικό μηχανισμό Ποιότητας Υπηρεσιών που εστιάζει στις υπηρεσίες Οχήματος-προς-Όλα (V2X). Το PreQoS είναι σε θέση να προβλέψει έγκαιρα συγκεκριμένες μετρήσεις Ποιότητας Υπηρεσιών, όπως ο ρυθμός δεδομένων uplink and downlink και η καθυστέρηση από άκρο σε άκρο, προκειμένου να προσφέρει το απαιτούμενο χρονικό διάστημα στο δίκτυο για την πιο αποτελεσματική κατανομή των πόρων του. Επιπλέον, η προληπτική διαχείριση αυτών των πόρων επιτρέπει στις αντίστοιχες υπηρεσίες και εφαρμογές του Οχήματος προς Όλα να εκτελούν εκ των προτέρων τυχόν ενδεχόμενες προσαρμογές που σχετίζονται με την Ποιότητα Υπηρεσιών. Η αξιολόγηση του προτεινόμενου μηχανισμού με βάση ένα ρεαλιστικό, προσομοιωμένο, συνδεδεμένο και αυτοματοποιημένο περιβάλλον κινητικότητας αποδεικνύει τη βιωσιμότητα και την εγκυρότητα μιας τέτοιας προσέγγισης.On the eve of 5G-enabled Connected and Automated Mobility, challenging Vehicle-to-Everything services have emerged towards safer and automated driving. The requirements that stem from those services pose very strict challenges to the network primarily with regard to the end-to-end delay and service reliability. At the same time, the in-network Artificial Intelligence that is emerging, reveals a plethora of novel capabilities of the network to act in a proactive manner towards satisfying the aforementioned challenging requirements. This thesis presents PreQoS, a predictive Quality of Service mechanism that focuses on Vehicle-to-Everything services. PreQoS is able to timely predict specific Quality of Service metrics, such as uplink and downlink data rate and end-to-end delay, in order to offer the required time window to the network to allocate more efficiently its resources. On top of that, the proactive management of those resources enables the respective Vehicle-to-Everything services and applications to perform any potential Quality of Service-related required adaptations in advance. The evaluation of the proposed mechanism based on a realistic, simulated, Connected and Automated Mobility environment proves the viability and validity of such an approach

Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements

The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures. The objective of our study is to provide a thorough analysis of predictive latency within 5G networks by utilizing real-world network data that is accessible to mobile network operators (MNOs). In particular, (i) we present an analytical formulation of the user-plane latency as a Hypoexponential distribution, which is validated by means of a comparative analysis with empirical measurements, and (ii) we conduct experimental results of probabilistic regression, anomaly detection, and predictive forecasting leveraging on emerging domains in Machine Learning (ML), such as Bayesian Learning (BL) and Machine Learning on Graphs (GML). We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events. Our results provide valuable insights into the efficacy of predictive algorithms in practical applications

Prediction Quality of Service in 5G Networks

Την παραμονή της συνδεδεμένης και αυτοματοποιημένης κινητικότητας (CAM) με δυνατότητα 5G, εμφανίστηκαν οι απαιτητικές υπηρεσίες όχημα-σε-οτιδήποτε (V2X) για αυτοματοποιημένη και ασφαλέστερη οδήγηση. Οι απαιτήσεις που απορρέουν από αυτές τις υπηρεσίες δημιουργούν πολύ αυστηρές προκλήσεις για το δίκτυο κυρίως όσον αφορά τον βασικό δείκτη απόδοσης (KPI) καθυστέρησης από άκρο σε άκρο (end-to-end delay). Ταυτόχρονα, η τεχνητή νοημοσύνη (AI) που εμφανίζεται εντός του δικτύου, αποκαλύπτει μια πληθώρα νέων δυνατοτήτων του δικτύου, να ενεργεί με προληπτικό τρόπο ως προς την ικανοποίηση των προαναφερθεισών απαιτήσεων. Αυτή η πτυχιακή εργασία παρουσιάζει έναν μηχανισμό πρόβλεψης ποιότητας υπηρεσιών (PreQoS), που υποστηρίζεται από τεχνητή νοημοσύνη, εστιάζει στις υπηρεσίες όχημα-σε-οτιδήποτε και είναι σε θέση να προβλέψει έγκαιρα συγκεκριμένες μετρήσεις ποιότητας υπηρεσίας. Παράδειγμα αυτών των υπηρεσιών είναι ο ρυθμός δεδομένων (data rate) και η καθυστέρηση στις ανερχόμενες (uplink) και κατερχόμενες ζεύξεις (downlink) από άκρο σε άκρο, προκειμένου να προσφέρει το απαιτούμενο χρονικό παράθυρο στο δίκτυο για να κατανείμει αποτελεσματικότερα τους πόρους του, καθώς και στις αντίστοιχες υπηρεσίες και εφαρμογές όχημα-σε-οτιδήποτε για την εκτέλεση των απαιτούμενων προσαρμογών. Η αξιολόγηση του προτεινόμενου μηχανισμού βασίζεται σε ένα ρεαλιστικό, προσομοιωμένο περιβάλλον όχημα-σε-οτιδήποτε που αποδεικνύει τη βιωσιμότητα και την εγκυρότητα μιας τέτοιας προσέγγισηςOn the eve of 5G-enabled Connected and Automated Mobility, challenging Vehicle-to-Everything services have emerged towards safer and automated driving. The requirements that stem from those services pose very strict challenges to the network primarily with regard to the end-to-end delay and service reliability. At the same time, the in-network Artificial Intelligence that is emerging, reveals a plethora of novel capabilities of the network to act in a proactive manner towards satisfying the aforementioned challenging requirements. This work presents PreQoS, a predictive Quality of Service mechanism that focuses on Vehicle-to-Everything services. PreQoS is able to timely predict specific Quality of Service metrics, such as uplink and downlink data rate and end to-end delay, in order to offer the required time window to the network to allocate more efficiently its resources. On top of that, the proactive management of those resources enables the respective Vehicle-to-Everything services and applications to perform any potential Quality of Service-related required adaptations in advance. The evaluation of the proposed mechanism based on a realistic, simulated, Connected and Automated Mobility environment proves the viability and validity of such an approach

Milestones in Autonomous Driving and Intelligent Vehicles Part \uppercase\expandafter{\romannumeral1}: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part \uppercase\expandafter{\romannumeral1} for this technical survey) to review the development of control, computing system design, communication, High Definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part \uppercase\expandafter{\romannumeral2} for this technical survey) is to review the perception and planning sections. The objective of this paper is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part \uppercase\expandafter{\romannumeral2}, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.Comment: 18 pages, 4 figures, 3 table

An LSTM-based network slicing classification future predictive framework for optimized resource allocation in C-V2X

With the advent of 5G communication networks, many novel areas of research have emerged and the spectrum of communicating objects has been diversified. Network Function Virtualization (NFV), and Software Defined Networking (SDN), are the two broader areas that are tremendously being explored to optimize the network performance parameters. Cellular Vehicle-to-Everything (C-V2X) is one such example of where end-to-end communication is developed with the aid of intervening network slices. Adoption of these technologies enables a shift towards Ultra-Reliable Low-Latency Communication (URLLC) across various domains including autonomous vehicles that demand a hundred percent Quality of Service (QoS) and extremely low latency rates. Due to the limitation of resources to ensure such communication requirements, telecom operators are profoundly researching software solutions for network resource allocation optimally. The concept of Network Slicing (NS) emerged from such end-to-end network resource allocation where connecting devices are routed toward the suitable resources to meet their requirements. Nevertheless, the bias, in terms of finding the best slice, observed in the network slices renders a non-optimal distribution of resources. To cater to such issues, a Deep Learning approach has been developed in this paper. The incoming traffic has been allocated network slices based on data-driven decisions as well as predictive network analysis for the future. A Long Short Term Memory (LSTM) time series prediction approach has been adopted that renders optimal resource utilization, lower latency rates, and high reliability across the network. The model will further ensure packet prioritization and will retain resource margin for crucial ones