Wireless InSite Academic Partnerships
With origins at Penn State University, Remcom has a longstanding partnership with the academic community. These collaborations not only foster shared research and development, but also improve our products and grow our expertise. Below are several examples demonstrating how Wireless InSite has been used in academic programs focused on advancing wireless mmWave technologies.
DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications
Machine learning tools are showing up in interesting applications in millimeter wave (mmWave) and massive MIMO systems. This is mainly due to their powerful capabilities in learning unknown models and tackling difficult optimization problems. To advance the machine learning research in mmWave/massive MIMO however, there is a need for a common dataset. This dataset can be used to evaluate the developed algorithms, reproduce the results, set benchmarks, and compare the different solutions. In this work, we introduce the DeepMIMO dataset, which is a generic dataset for mmWave/massive MIMO channels. The DeepMIMO dataset generation framework has two important features. First, the DeepMIMO channels are constructed based on accurate ray-tracing data obtained from Wireless InSite. The DeepMIMO channels, therefore, capture the dependence on the environment geometry/materials and transmitter/receiver locations, which is essential for several machine learning applications. Second, the DeepMIMO dataset is generic/parameterized, allowing the researcher to adjust a set of system and channel parameters to tailor the generated DeepMIMO dataset for the target machine learning application.
RAYMOBTIME
Raymobtime is a methodology for collecting realistic datasets for simulating wireless communications. It uses ray-tracing and 3D scenarios with mobility and time evolution for obtaining consistency over time, frequency, and space. It incorporates simulations of LIDAR (via Blensor), cameras (via Blender), and positions to enable investigations using machine learning and other techniques. Raymobtime has used Wireless Insite for ray-tracing and the open source Simulator of Urban Mobility (SUMO) for mobility simulation (of vehicles, pedestrians, drones, etc). It also incorporates Cadmapper and Open Street Map to simplify importing realistic outdoor scenarios. For more details, please visit Raymobtime’s website publications.
ITU Artificial Intelligence/Machine Learning in 5G Challenge
research.ece.ncsu.edu/ai5gchallenge
The ML5G-PHY channel estimation challenge attacks one of the most difficult problems in the 5G physical layer: acquiring channel information to establish a mmWave MIMO link (initial access) considering a hybrid MIMO architecture. Approaches in the challenge will lead to important insights into what can be achieved using data-driven and/or model-based approaches.
Drexel Wireless Systems Laboratory
research.coe.drexel.edu/ece/dwsl/research/mmwave-research
Based on promising simulation results from Wireless InSite, Drexel Wireless Systems Laboratory is expanding their research to cover the antenna design at mmWave frequencies and to conduct measurement campaigns to characterize the channel in various practical settings.