Utilization of Deep Learning Methods for Object Detection to Camera Data collected from vehicle’s Reference Sensors

The Autonomous Drive (AD) systems and Advanced Driver Assistance Systems (ADAS) in the current and future generations of vehicles include a large number of sensors which are used to perceive the vehicle’s surroundings. The production sensors of these vehicles are verified and validated against reference data that are originated from high-accurate reference sensors that are placed in a reference roof box at the top of the vehicle. In this thesis, are explored ways to strengthen the reference camera data by applying deep machine learning algorithms together with other techniques for 2D object detection. For this reason, they are used two driving related datasets, public Berkeley DeepDrive dataset (BDD100K) and Volvo’s annotated data. Also they are trained and evaluated two state of the art deep learning algorithms for object detection, Mask R-CNN [5] and YOLOv4 [25]. Finally, it is implemented in conjunction, a semi-supervised technique to improve the predictive performance using unlabeled data. The utilized semi-supervised learning framework is called STAC and it is introduced in the paper A Simple Semi-Supervised Learning Framework for Object Detection [27]

Coverage Extension as a Service for Flexible 6G Networks Infrastructure

In future 6G wireless networks, it is important to ensure that equal opportunity is offered to citizens and businesses regardless of location with a dynamic and efficient expansion of the infrastructure. Dynamic coverage and connectivity extension mechanisms exploiting multiple types of Mobile Access Points (MAPs) during a short amount of time for covering areas that cannot be easily reached, are developed by DEDICAT 6G project. This service is called Coverage Extension as a Service (CEaaS). This paper proposes a system architecture for CEaaS and the functionalities that the DEDICAT 6G platform will offer. This includes context awareness (i.e. knowledge about users and technology recognition), coverage extension decision making (i.e. MAP and swarm operation) and network operation decision making (i.e. MAP-user association and radio access technology selection). Furthermore, performanc

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

The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven Communication and Computation co-design for 6G

International audienceThis paper provides an overview of the most recent advancements and outcomes of the European 6G flagship project Hexa-X, on the topic of in-network Artificial Intelligence (AI) and Machine Learning (ML). We first present a general introduction to the project and its ambitions in terms of use cases (UCs), key performance indicators (KPIs), and key value indicators (KVIs). Then, we identify the key challenges to realize, implement, and enable the native integration of AI and ML in 6G, both as a means for designing flexible, low-complexity, and reconfigurable networks (\textit{learning to communicate}), and as an intrinsic in-network intelligence feature (\textit{communicating to learn }or, 6G as an efficient AI/ML platform). We present a high level description of down selected technical enablers and their implications on the Hexa-X identified UCs, KPIs and KVIs. Our solutions cover lower layer aspects, including channel estimation, transceiver design, power amplifier and distributed MIMO related challenges, and higher layer aspects, including AI/ML workload management and orchestration, as well as distributed AI. The latter entails Federated Learning and explainability as means for privacy preserving and trustworthy AI. To bridge the gap between the technical enablers and the 6G targets, some representative numerical results accompany the high level description. Overall, the methodology of the paper starts from the UCs and KPIs/KVIs, to then focus on the proposed technical solutions able to realize them. Finally, a brief discussion of the ongoing regulation activities related to AI is presented, to close our vision towards an AI and ML-driven communication and computation co-design for 6G

The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven Communication and Computation co-design for 6G

International audienceThis paper provides an overview of the most recent advancements and outcomes of the European 6G flagship project Hexa-X, on the topic of in-network Artificial Intelligence (AI) and Machine Learning (ML). We first present a general introduction to the project and its ambitions in terms of use cases (UCs), key performance indicators (KPIs), and key value indicators (KVIs). Then, we identify the key challenges to realize, implement, and enable the native integration of AI and ML in 6G, both as a means for designing flexible, low-complexity, and reconfigurable networks (\textit{learning to communicate}), and as an intrinsic in-network intelligence feature (\textit{communicating to learn }or, 6G as an efficient AI/ML platform). We present a high level description of down selected technical enablers and their implications on the Hexa-X identified UCs, KPIs and KVIs. Our solutions cover lower layer aspects, including channel estimation, transceiver design, power amplifier and distributed MIMO related challenges, and higher layer aspects, including AI/ML workload management and orchestration, as well as distributed AI. The latter entails Federated Learning and explainability as means for privacy preserving and trustworthy AI. To bridge the gap between the technical enablers and the 6G targets, some representative numerical results accompany the high level description. Overall, the methodology of the paper starts from the UCs and KPIs/KVIs, to then focus on the proposed technical solutions able to realize them. Finally, a brief discussion of the ongoing regulation activities related to AI is presented, to close our vision towards an AI and ML-driven communication and computation co-design for 6G

The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven communication and computation co-design for 6G

Abstract This paper provides an overview of the most recent advancements and outcomes of the European 6G flagship project Hexa-X, on the topic of in-network Artificial Intelligence (AI) and Machine Learning (ML). We first present a general introduction to the project and its ambitions in terms of use cases (UCs), key performance indicators (KPIs), and key value indicators (KVIs). Then, we identify the key challenges to realize, implement, and enable the native integration of AI and ML in 6G, both as a means for designing flexible, low-complexity, and reconfigurable networks ( learning to communicate ), and as an intrinsic in-network intelligence feature ( communicating to learn or, 6G as an efficient AI/ML platform). We present a high level description of down selected technical enablers and their implications on the Hexa-X identified UCs, KPIs and KVIs. Our solutions cover lower layer aspects, including channel estimation, transceiver design, power amplifier and distributed MIMO related challenges, and higher layer aspects, including AI/ML workload management and orchestration, as well as distributed AI. The latter entails Federated Learning and explainability as means for privacy preserving and trustworthy AI. To bridge the gap between the technical enablers and the 6G targets, some representative numerical results accompany the high level description. Overall, the methodology of the paper starts from the UCs and KPIs/KVIs, to then focus on the proposed technical solutions able to realize them. Finally, a brief discussion of the ongoing regulation activities related to AI is presented, to close our vision towards an AI and ML-driven communication and computation co-design for 6G