ERI: Towards Environment-Aware Wireless Communications for Next Generation Networks

Next-generation networks are expected to deliver ultra-low-latency and high-speed data-rates to support new technology and applications such as healthcare immersive technology, virtual/augmented reality, and smart city applications. To meet the unprecedented demand for broadband access, this project will leverage high-resolution environment sensory data to infer location-based channel characteristics, predict link blockages beforehand, and enable robust mm-wave communications in dynamic indoor environments.

System-level design of the proposed link configuration algorithms will be developed and analyzed. The proposed algorithms are expected to enhance the spectrum usability and access to indoor mm-wave wireless networks and enable emerging technology applications. This research will be conducted at a primarily undergraduate institution and will impact a significant number of students in the classroom and through direct research experience with the goal of broadening their engagement and promoting their retention. The research findings will be disseminated in scientific conferences and journal publications.

The main goal of this project is to enable high-speed and ultra-reliable mm-wave communications in realistic indoor environments. To this achieve this, new transmission strategies that make use of Lidar and mm-wave sensory data fusion will be developed. The overall project objectives are: (1) design and develop low complexity and latency link configuration algorithms for mm-wave systems suitable for dynamic indoor environments based Lidar and mm-wave sensory data; (2) investigate the use of Lidar intensity data for classification of common indoor surfaces for beyond line-of-sight mm-wave communication; (3) leverage advanced array processing and signal processing tools to develop transmission strategies that adapts to environment clutter; and (4) develop early blockage warning algorithms for mm-wave communication links based on Lidar sensing.

The expected research findings will lead to new data-driven channel models and signal processing and machine learning approaches that mitigate mm-wave link configuration challenges in dynamic environments.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.