Hexa-X the European 6G Flagship Project

Hexa-X will pave the way to the next generation of wireless networks (Hexa) by explorative research (X). The Hexa-X vision is to connect human, physical, and digital worlds with a fabric of sixth generation (6G) key enablers. The vision is driven by the ambition to contribute to objectives of growth, global sustainability, trustworthiness, and digital inclusion. Key 6G value indicators and use cases are defined against the background of technology push, society and industry pull as well as objectives of technology sovereignty. Key areas of research have been formulated accordingly to include connecting intelligence, network of networks, sustainability, global service coverage, extreme experience, and trustworthiness. Critical technology enablers for 6G are developed in the project including, sub-THz transceiver technologies, accurate stand-alone positioning and radio-based imaging, improved radio performance, artificial intelligence (AI) / machine learning (ML) inspired radio access network (RAN) technologies, future network architectures and special purpose solutions including future ultra-reliable low-latency communication (URLLC) schemes. Besides technology enablers, early trials will be carried out to help assess viability and performance aspects of the key technology enablers. The 6G Hexa-X project is integral part of European and global research effort to help define the best possible next generation of networks

Hexa-X the European 6G Flagship Project

Hexa-X will pave the way to the next generation of wireless networks (Hexa) by explorative research (X). The Hexa-X vision is to connect human, physical, and digital worlds with a fabric of sixth generation (6G) key enablers. The vision is driven by the ambition to contribute to objectives of growth, global sustainability, trustworthiness, and digital inclusion. Key 6G value indicators and use cases are defined against the background of technology push, society and industry pull as well as objectives of technology sovereignty. Key areas of research have been formulated accordingly to include connecting intelligence, network of networks, sustainability, global service coverage, extreme experience, and trustworthiness. Critical technology enablers for 6G are developed in the project including, sub-THz transceiver technologies, accurate stand-alone positioning and radio-based imaging, improved radio performance, artificial intelligence (AI) / machine learning (ML) inspired radio access network (RAN) technologies, future network architectures and special purpose solutions including future ultra-reliable low-latency communication (URLLC) schemes. Besides technology enablers, early trials will be carried out to help assess viability and performance aspects of the key technology enablers. The 6G Hexa-X project is integral part of European and global research effort to help define the best possible next generation of networks

Statistical appliance inference in the smart grid by machine learning

Smart Grid has been attracting more interest than ever thanks to emergence of enabling technologies such as 5G and IoT. Yet, there are some long-standing privacy concerns about revealing habits and lifestyles of people from fine-grained power consumption data collected through smart meters. In this context, the contribution of this work is twofold: First, we empirically demonstrate how appliance-level fine-grained power consumption data can reveal households' routines simply using probability density estimations derived from consumption data without requiring any complex analysis. Second, we point out that appliance types can be identified in a targeted house using circuit-level consumption data of other houses. We show how machine learning can be used maliciously to realize this threat in an automatic manner and achieve high success rate even with limited amount of training data on the public REDD dataset. In addition, we provide discussions on possible countermeasures against the threats examined in this study

Differentially private deep learning for load forecasting on smart grid

Load forecasting is vital for a reliable and sustainable smart grid as it is used to predict the demand and make price adjustment accordingly. Electric consumption data which is gathered from IoT devices like smart meter or smart appliances is a key input to improve the accuracy of the forecasting task. However, this data can leak private information of the householders as the consumption data reflects the behavioral patterns of the individuals. Providing privacy for the data without compromising the utility of the forecast is a challenging problem and this is where the differential privacy comes in to play. In this work, we present a practical implementation of the privacy preserving load forecasting with differential privacy techniques using Tensorflow Privacy library. We show that privacy guarantee for the data can be achieved to varying degrees with a tolerable degradation in the forecast results. We provide privacy-utility tradeoff values in our experiments for different privacy levels

Vulnerability Prediction from Source Code Using Machine Learning

As the role of information and communication technologies gradually increases in our lives, software security becomes a major issue to provide protection against malicious attempts and to avoid ending up with noncompensable damages to the system. With the advent of data-driven techniques, there is now a growing interest in how to leverage machine learning (ML) as a software assurance method to build trustworthy software systems. In this study, we examine how to predict software vulnerabilities from source code by employing ML prior to their release. To this end, we develop a source code representation method that enables us to perform intelligent analysis on the Abstract Syntax Tree (AST) form of source code and then investigate whether ML can distinguish vulnerable and nonvulnerable code fragments. To make a comprehensive performance evaluation, we use a public dataset that contains a large amount of function-level real source code parts mined from open-source projects and carefully labeled according to the type of vulnerability if they have any.We show the effectiveness of our proposed method for vulnerability prediction from source code by carrying out exhaustive and realistic experiments under different regimes in comparison with state-of-art methods

A network-based positioning method to locate false base stations

In recent years False Base Stations (FBSs) have received increased attention. A False Base Station can perform active or passive attacks against mobile devices or user equipment (UE) to steal private information, such as International Mobile Subscriber Identifier (IMSI), to trace users locations, or to prevent users from getting service from operators. Most of the existing solutions related to FBS have focused on the detection aspects of the false station rather than locating its position. However, once an FBS is detected in a network, discovering its exact location precisely and remotely becomes highly crucial to initiate preventive actions. In this work, we propose a network-based localization method for estimating the exact geographical position of an FBS whose existence is already detected in a cellular network. Our method relies on a comparative pairwise analysis of the Reference Signals Received Power (RSRP) values reported as a standard procedure by the UEs in the vicinity of FBS through their measurement reports. Specifically, for each pair of related measurement reports, we identify a half-plane indicating the probable location of the FBS and then predict the exact location based on the intersection of all obtained half-planes. We have implemented and experimentally evaluated our proposed method in the Network Simulator 3 (ns-3) and showed that it accurately estimates FBS location with meter-level precision under different scenarios in a cellular network

6G E2E Architecture Framework with Sustainability and Security Considerations

The research on 6G in the EU-funded flagship project Hexa-X started with the investigation of the most important technology enablers and the evaluation of relevant 6G use cases. The next step is to integrate these enablers in a 6G E2E architecture that fulfills all use case-based Key Performance (KPI) and Key Value Indicators (KVI) and that follows the guidelines of general architectural principles. In addition, the main focus of an E2E 6G architecture must be on security and sustainability which both will have increased importance for future communication networks and society

Pervasive artificial intelligence in next generation wireless: The Hexa-X project perspective

The European 6G flagship project Hexa-X has the objective to conduct exploratory research on the next generation of mobile networks with the intention to connect human, physical and digital worlds with a fabric of technology enablers. Within this scope, one of the main research challenges is the ambition for beyond 5G (B5G)/6G systems to support, enhance and enable real-time trustworthy control by transforming Artificial Intelligence (AI) / Machine Learning (ML) technologies into a vital and trusted tool for large-scale deployment of interconnected intelligence available to the wider society. Hence, the study and development of concepts and solutions enabling AI-driven communication and computation co-design for a B5G /6G communication system is required. This paper focuses on describing the possibilities that emerge with the application of AI/ML mechanisms to 6G networks, identifying the resulting challenges and proposing some potential solution approaches

Hexa-X:the European 6G flagship project

Abstract Hexa-X will pave the way to the next generation of wireless networks (Hexa) by explorative research (X). The Hexa-X vision is to connect human, physical, and digital worlds with a fabric of sixth generation (6G) key enablers. The vision is driven by the ambition to contribute to objectives of growth, global sustainability, trustworthiness, and digital inclusion. Key 6G value indicators and use cases are defined against the background of technology push, society and industry pull as well as objectives of technology sovereignty. Key areas of research have been formulated accordingly to include connecting intelligence, network of networks, sustainability, global service coverage, extreme experience, and trustworthiness. Critical technology enablers for 6G are developed in the project including, sub-THz transceiver technologies, accurate stand-alone positioning and radio-based imaging, improved radio performance, artificial intelligence (AI) / machine learning (ML) inspired radio access network (RAN) technologies, future network architectures and special purpose solutions including future ultra-reliable low-latency communication (URLLC) schemes. Besides technology enablers, early trials will be carried out to help assess viability and performance aspects of the key technology enablers. The 6G Hexa-X project is integral part of European and global research effort to help define the best possible next generation of networks