ECE PhD Proposal Review: Miead Tehrani-Moayyed

PhD Proposal Review: RF Channel Models for Static and Mobile Scenarios: From Simulations to Models for Large-scale Emulations

Miead Tehrani-Moayyed

Location: ISEC 432

Abstract: The extremely high data rates provided by communications at higher frequency bands, e.g., millimeter waves (mmWave), can help address the unprecedented demands of next-generation wireless networks. However, as several impairments limit wireless coverage at higher frequencies, accurate models of wireless scenarios and testing at scale are needed to show actual potential and to realize the promises that the new wireless technologies can bring forth. Large-scale accurate simulations and wireless networks emulators are now a time and cost-effective solution to perform these tests in a lab before deployment in the field. This dissertation work focuses on modeling, calibration, and validation of realistic RF scenarios for wireless network emulation at scale.
The contributions of our work include (i) investigating the characteristic of the wireless channel at higher frequencies (mmWave) and the performance evaluation of mmWave communications on top of the recently released NR standard for 5G cellular networks, and (ii) a framework to create RF scenarios for emulators like \emph{Colosseum} starting from rich forms of input, like those obtained by ray-tracers or via real-field measurements.
(i) We derive channel propagation models via ray-tracing simulations for mmWave transmissions with applications to vehicle-to-everything (V2X) communications. We analyze aspects related to blockage modeling, the effects of antenna beamwidth, beam alignment, and multipath fading in urban scenarios and emphasize the importance of capturing diffuse scattered rays for improved large-scale and small-scale radio channel propagation models. Furthermore, we compare the performance of mmWave 5G NR with the 4G long-term evolution (LTE) standard on a realistic environment and show the impact of MIMO technology to improve the performance of 5G NR cellular networks. As transmitted radio signals are received as clusters of multipath rays, identifying these clusters provides better spatial and temporal characteristics of the channel. We deal with the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. We analyze how the clustering solution changes with narrower-beam antennas, and we provide a comparison of the cluster characteristics for different types of antennas.
(ii) Our framework to model wireless scenarios for large-scale emulators optimally scales down the large set of RF data in input to the fewer parameters allowed by the emulator by using efficient clustering techniques and channel impulse response re-sampling. We demonstrate the effectiveness of the proposed framework through modeling realistic scenarios for Colosseum starting from the rich input from a commercial-grade ray-tracing software: Wireless Insite by Remcom. We propose to finish our investigation (a)~by introducing ways of dealing with mobility in emulated scenarios, and to perform adequate channel sounding to validate them, and (b)~by indicating ways to provide input to the emulator through actual wireless measurements in the field. Particularly, as campaigns in the field provide measurements for a sparse set of locations, we plan to use deep learning techniques to “interpolate” channel parameters for a larger set of locations, determining the trade-offs for achieving desired accuracy and reasonable computational requirements.