6G White Paper on Localization and Sensing
6G Research Visions, No. 12, led by Carlos de Lima
This white paper explores future localization and sensing opportunities for beyond fifth generation(5G) wireless communication systems by identifying key technology enablers and discussing their underlying challenges, implementation issues, and identifying potential solutions. In addition, we present exciting new opportunities for localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. In contrast to 5G and earlier generations, localization and sensing will be built-in from the outset to both cope with specific applications and use cases, and to support flexible and seamless connectivity.
Following the trend initiated in the 5G new radio (NR) systems, sixth generation (6G) will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler and angular resolutions, as well as localization to cm-level degree of accuracy. Moreover, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems.
6G systems will be truly intelligent wireless systems that will not only provide ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency and space. Applications such as THz imaging and spectroscopy have the potential to provide continuous, real-time physiological information via dynamic, non-invasive, contactless measurements for future digital health technologies. 6G simultaneous localization and mapping (SLAM) methods will not only enable advanced cross reality (XR) applications but also enhance the navigation of autonomous objects such as vehicles and drones. In convergent6G radar and communication systems, both passive and active radars will simultaneously use and share information, to provide a rich and accurate virtual image of the environment. In 6G, intelligent context-aware networks will be capable of exploiting localization and sensing information to optimize deployment, operation, and energy usage with no or limited human intervention.
This white paper concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust. Addressing these challenges will undoubtedly require an inter-disciplinary and concerted effort from the research community.
This white paper has been written by an international expert group, led by the Finnish 6G Flagship program at the University of Oulu, within a series of twelve 6G white papers published in their final format in 2020.
Watch the Webinar
- We focus on the key aspects of the localization and sensing procedures by identifying potential enabling technologies and main features, assessing new opportunities of the environment-aware applications and recommending latest trends while posing key research questions.
- 6G systems will be intelligent context-aware networks exploiting built-in localization and sensing features to enhance communication with no or limited human intervention.
- 6G systems will achieve high-accuracy positioning and high-resolution sensing/imaging enabling autonomous navigation and advanced XR applications with rich and accurate virtual imagery of the environment.
- Carlos de Lima, University of Oulu, Finland
- Didier Belot, CEA-LETI, France
- Rafael Berkvens. University of Antwerp – IMEC, Belgium
- Andre Bourdoux, IMEC, Belgium
- Davide Dardari, University of Bologna, Italy
- Maxime Guillaud, Huawei Technologies Paris, France
- Minna Isomursu, University of Oulu, Finland
- Elena-Simona Lohan, Tampere University, Finland
- Yang Miao, University of Twente, Netherlands
- Andre Noll Barreto, Barkhausen Institut, Germany
- Muhammad Reza Kahar Aziz, Institut Teknologi Sumatera, Indonesia
- Jani Saloranta, University of Oulu, Finland
- Tachporn Sanguanpuak, University of Oulu, Finland
- Hadi Sarieddeen, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
- Gonzalo Seco-Granados, Universitat Autonoma de Barcelona, Spain
- Jaakko Suutala, University of Oulu, Finland
- Tommy Svensson, Chalmers University of Technology, Sweden
- Mikko Valkama, Tampere University, Finland
- Barend Van Liempd, IMEC, Belgium
- Henk Wymeersch, Chalmers University of Technology, Sweden