Miead Tehrani Moayyed PhD Dissertation Defense

Name:
Miead Tehrani Moayyed

Title:
RF Channel Models for Static and Mobile Scenarios: From Simulations to Models for Large-scale Emulations and Digital Twins

Date:
7/22/2024

Time:
1:00:00 PM

Location:
Room: EXP-601A

Committee Members:
Prof. Stefano Basagni (Advisor)
Prof. Tommaso Melodia
Prof. Milica Stojanovic

Abstract:
The extremely high data rates provided by communications at higher frequency bands, such as mmWave, can address the unprecedented demands of next-generation wireless networks. However, several impairments limit wireless coverage at higher frequencies, necessitating accurate models of wireless scenarios and large-scale testing to test and realize the potential of these new technologies. Large-scale accurate simulations and wireless network emulators now offer a time- and cost-effective solution for performing these tests in a lab before field deployment. This dissertation focuses on modeling, calibration, and validation of realistic RF scenarios for wireless network emulation at scale. The contributions of this work include: (i) Investigating the characteristics of the wireless channel at higher frequencies (mmWave) and evaluating the performance of mmWave communications on top of the NR standard for 5G cellular networks; (ii) developing a streamlined framework to create realistic RF scenarios with mobility support for Finite Input Response (FIR)-based emulators like Colosseum, starting from rich inputs such as precise ray tracing methods or real-field measurements, and (iii) creating an accurate AI-assisted propagation model that integrates joint measurements and simulations, achieving the desired accuracy and reasonable computational requirements for real-time Digital Twin (DT) wireless networks. Particularly:

(i) We derive channel propagation models via ray tracing simulations for mmWave transmissions with applications to V2X communications. We analyze aspects related to blockage modeling, the effects of antenna beamwidth, beam alignment, and multipath fading in urban scenarios, emphasizing 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 in a realistic environment and demonstrate the impact of MIMO technology on improving 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 address 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 provide a comparison of the cluster characteristics for different types of antennas.

(ii) Our framework for modeling wireless scenarios for large-scale emulators optimally scales down the large set of channel input to the fewer parameters allowed by the emulator using efficient clustering techniques and Channel-Impulse Response (CIR) re-sampling. We demonstrate the effectiveness of the proposed framework by modeling realistic scenarios for Colosseum, starting with rich input from commercial-grade ray tracing software, Wireless InSite (WI) by Remcom. To support mobility, we implement a mobile channel simulator on top of the WI ray-tracer, consisting of two steps: (a) spatially sampling the mobile channels using the ray-tracer, and (b) parsing the ray tracing outputs to extract the channels for each time instant of emulation. We also develop a Software-Defined Radio (SDR)-based channel sounder to precisely characterize emulated RF channels. The sounder framework is fully containerized, scalable, and automated to capture the gains and delays of the channel CIR taps.

(iii) We extend these efforts to develop the first Digital Twins for Mobile Networks (DTMN) on Colosseum, using the RF testbed Arena as a use case. This use case demonstrates the scope and capabilities of Colosseum as a DT, providing the research community with a set of tools to replicate real-world environments. We compare key network performance metrics, namely throughput and SINR, of the Arena/Colosseum DTMN to validate the fidelity of our twinning process. Furthermore, we present an AI-assisted propagation model to generate realistic, real-time, and scalable scenarios for DTMNs. This model seamlessly integrates measurements with ray tracing, providing a high-resolution, realistic channel model. We study the computational complexity and configuration trade-offs associated with ray tracing for high-fidelity prediction, generating a large dataset to train this enhanced AI model. Our proof of concept highlights the accuracy and generalization capabilities of our AI model across previously unseen transmitter (TX) locations and unfamiliar environments, outperforming state-of-the-art approaches and achieving significant improvements in accuracy. We analyze the computational complexity of our AI model, comparing it to high-fidelity ray tracing. Profiling reveals a three-order-of-magnitude acceleration, enabling real-time propagation prediction with reasonable accuracy. We explore key ray tracing parameters contributing to the discrepancy between measurements and simulations and demonstrate the integration of measurements into channel prediction, thereby calibrating the model.