Near-field scanning (NFS) is an important technique in evaluating the radiation and emission of electromagnetic (EM) fields originating from microwave devices and components. Over the past decades, considerable progresses have been made in enhancing scanning capabilities, including the usage of robotics, high-speed instrument communication, and high-sampling rate equipment. Despite these advancements, the time and cost of measurements are still limited by requests for large-scale, high-resolution, magnitude, and phase scanning, particularly in integrated-circuit (IC) and antenna applications concerning electromagnetic compatibility (EMC) tests.

This project aims to significantly reduce the time and cost of traditional Near-Field Scanning (NFS) by tenfold without compromising accuracy. This will be accomplished through the utilization of an innovative single-probe on-the-fly (OTF) data acquisition and processing technique. As a proof of concept, a coil antenna example has undergone testing, as depicted in (a) and (b). The technique will be extended for cost-effective indoor and outdoor EMC testing up to GHz frequencies.

Contact: Dr. Cheng Yang (Senior Engineer)

As 6G technologies continue to advance, the need to accurately monitor and manage wireless signal behavior in complex urban environments becomes increasingly critical. Under the umbrella of the Hanseatic (formerly Hamburg) Wireless Innovation Competence Center (HAWICC), one of four locations nationwide of the German Center for Future Mobility (DZM), funded by the Federal Ministry for Digital and Transport, the “Next Generation City Networking” (NGCN) project aims to explore an innovative solution using wave field synthesis for future mobility, enabling high-bandwidth, low-latency wireless communication. As part of the NGCN project, this research focuses on developing advanced mobile field measurement techniques for radio channels in realistic communication environments. Vast amounts of data provided by drones and fixed 6G antennas are collected and analyzed using machine learning techniques, allowing the system to adjust wave propagation and signal parameters for optimal performance. The work includes the design of mobile sensors, modeling of wireless channels, and validation of the system in real-world test environments. The results support future applications in emergency communications, intelligent transportation infrastructure, and reliable in-city connectivity requiring accurate and stable coverage.

Funding: Bundesministerium für Digitales und Verkehr
Contact: Abidur Rahman, M.Sc.
Start: 02.05.2025