6ET-1: 6G Technologies I
Wednesday, 8 June 2022, 10:30-12:00
Session Chair: Tbd (, )
Setting 6G Architecture in Motion – the Hexa-X Approach
Stefan Wänstedt and Mårten Ericson (Ericsson Research, Sweden); Merve Saimler (Ericsson Research, Turkey); Panagiotis Vlacheas (WINGS ICT SOLUTIONS, Greece); Damiano Rapone (Telecom Italia, Italy); Antonio de la Oliva and Carlos J. Bernardos (Universidad Carlos III de Madrid, Spain); Riccardo Bassoli (Technische Universität Dresden, Germany); Frank H.P. Fitzek (Technische Universität Dresden & ComNets – Communication Networks Group, Germany); Giovanni Nardini (University of Pisa, Italy); Gerald Kunzmann (Nokia, Germany); Hannu Flinck (Nokia Bell Labs, Finland); Miltiadis C. Filippou (Intel Germany GmbH, Germany); Panagiotis Demestichas (National Technical University of Athens, Greece); Markus Dominik Mueck (Intel Deutschland GmbH, Germany)
The most recent cellular generation, 5G, is being deployed on a large scale globally. The capabilities of 5G surpass all previous generations of cellular networks and support many new services compared to 4G. Despite this, at the same time, preparations for 6G have begun since user demands and technical development continuously push the boundaries of what is possible. Demands come not only from users. Also, society sets requirements, e.g., sustainability, coverage, and privacy. To support the necessary features in the network needed to meet the requirements, a new generation of the architecture is needed; one based on the most forward-looking design principles together with trends in networks, use cases, and whatnot. To show that the proposed new features will allow the future network to meet the set requirements, key performance indicators (KPIs) have to be defined. In this paper, we present six of the KPIs that the European 6G flagship project Hexa-X has identified as the fundamental ones to measure the most important aspects of a new 6G architecture.
Effective Goal-Oriented 6G Communications: The Energy-Aware Edge Inferencing Case
Mattia Merluzzi (CEA-Leti, France); Miltiadis C. Filippou (Intel Germany GmbH, Germany); Leonardo Gomes Baltar (Intel Deutschland GmbH, Germany); Emilio Calvanese Strinati (CEA-LETI, France)
Currently, the world experiences an unprecedentedly increasing generation of application data, from sensor measurements to video streams, thanks to the extreme connectivity capability provided by 5G networks. Going beyond 5G technology, such data aim to be ingested by Artificial intelligence functions instantiated in the network to facilitate informed decisions, essential for the operation of applications, such as automated driving and factory automation. Nonetheless, while computing platforms hosting Machine Learning models are ever powerful, their energy footprint is a key impeding factor towards realizing a wireless network as a sustainable intelligent platform. Focusing on a beyond 5G wireless network, overlaid by Multi-access Edge Computing (MEC) infrastructure with inferencing capabilities, our paper tackles the problem of energy-aware dependable inferencing by considering inference effectiveness as value of a goal that needs to be accomplished by paying the minimum price in energy consumption. Both MEC-assisted standalone and ensemble inference options are evaluated. It is shown that, for some system scenarios, goal effectiveness above 84% is achieved and sustained even by relaxing communication reliability requirements by one decimal digit, while, enjoying a device radio energy consumption reduction of almost 23% at the same time. Also, ensemble inference is shown to improve system-wide energy efficiency and even achieve higher goal effectiveness, as compared to the standalone case for some system parameterizations.
6G Radio Requirements to Support Integrated Communication, Localization, and Sensing
Henk Wymeersch (Chalmers University of Technology, Sweden); Aarno Pärssinen (University of Oulu, Finland); Traian E Abrudan (Nokia Bell Labs, Finland); Andreas Wolfgang (Qamcom Research & Technology AB, Sweden); Katsuyuki Haneda (Aalto University, Finland); Muris Sarajlic (Lund University, Sweden); Marko E Leinonen (University of Oulu, Finland); Musa Furkan Keskin and Hui Chen (Chalmers University of Technology, Sweden); Simon Lindberg (Qamcom Research & Technology, Sweden); Pekka Kyösti (Keysight Technologies & University of Oulu, Finland); Tommy Svensson (Chalmers University of Technology, Sweden); Xinxin Yang (Qamcom Research and Technology, Sweden)
6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing. The use cases can be directly mapped to requirements in terms of standard key performance indicators (KPIs), such as data rate, latency, or localization accuracy. The goal of this paper is to go one step further and map these standard KPIs to requirements on signals, on hardware architectures, and on deployments. Based on this, system solutions can be identified that can support several use cases simultaneously. Since there are several ways to meet the KPIs, there is no unique solution and preferable configurations will be discussed.
Demonstration of Smart Identification Sensors for Future 6G Intelligent IoT Applications
Grishma Khadka (Deakin University, Australia); Larry M Arjomandi (Monash University, Australia); Nemai Karmakar (MONASH University, Australia); Jinho Choi (Deakin University, Australia)
In this paper, we present a demonstration using chipless smart identification (CSID) sensor for Internet of Things (IoT) applications with an aim to develop a potential application scenario to anticipate future 6G intelligent service systems. The proposed system uses an innovative CSID sensor and mobile data collector (MoDaC) with physical detection features. It is based on backscatter communication (BackCom), having low power consumption capability and low implementation cost. The feasibility of different CSID sensor system components is evaluated using the real-time experiment when it is attached to various retail objects having different physical characteristics, which is further performed in a moving scenario. The result shows that the selected system components significantly detect the sensor information in a robust environment. We also explore the challenges and suggest potential future work to support the vision of using CSID sensors for massive data collection.
Map-Assisted Material Identification at 100 GHz and Above Using Radio Access Technology
Yi Geng (UNISOC, China)
The inclusion of material identification in wireless communication system is an emerging area that offers many opportunities for 6G systems. By using reflected radio wave to determine the material of reflecting surface, not only the performance of 6G networks can be improved, but also some exciting applications can be developed. In this paper, we recap a few prior methods for material identification, then analyze the impact of thickness of reflecting surface on reflection coefficient and present a new concept “settling thickness”, which indicates the minimum thickness of reflecting surface to induce steady reflection coefficient. Finally, we propose a novel material identification method based on ray-tracing and 3D-map. Compared to some prior methods that can be implemented in single-bounce-reflection scenario only, we extend the capability of the method to multiple-bounce-reflection scenarios.