arXiv subjects

Miead Tehrani-Moayyed

Publications and source records attributed to Miead Tehrani-Moayyed.

5 recordsLinked to original sources

arXiv2026

AIRMap: AI-Generated Radio Maps for Wireless Digital Twins

Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications. Traditional modeling methods like ray tracing are however computationally demanding and unsuited to model dynamic conditions. In this paper, we propose AIRMap, a deep-learning framework for ultra-fast radio-map estimation, along with an automated pipeline for creating the largest radio-map dataset to date. AIRMap uses a single-input U-Net autoencoder that processes only a 2D elevation map of terrain and building heights. Trained on 1.2M Boston-area samples and validated across four distinct urban and rural environments with varying terrain and building density, AIRMap predicts path gain with under 4 dB RMSE in 4 ms per inference on an NVIDIA L40S-over 100x faster than GPU-accelerated ray tracing based radio maps. A lightweight calibration using just 20% of field measurements reduces the median error to approximately 5%, significantly outperforming traditional simulators, which exceed 50% error. Integration into the Colosseum emulator and the Sionna SYS platform demonstrate near-zero error in spectral efficiency and block-error rate compared to measurement-based channels. These findings validate AIRMap's potential for scalable, accurate, and real-time radio map estimation in wireless digital twins.

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arXiv2024

AI-assisted Agile Propagation Modeling for Real-time Digital Twin Wireless Networks

Accurate channel modeling in real-time faces remarkable challenge due to the complexities of traditional methods such as ray tracing and field measurements. AI-based techniques have emerged to address these limitations, offering rapid, precise predictions of channel properties through ground truth data. This paper introduces an innovative approach to real-time, high-fidelity propagation modeling through advanced deep learning. Our model integrates 3D geographical data and rough propagation estimates to generate precise path gain predictions. By positioning the transmitter centrally, we simplify the model and enhance its computational efficiency, making it amenable to larger scenarios. Our approach achieves a normalized Root Mean Squared Error of less than 0.035 dB over a 37,210 square meter area, processing in just 46 ms on a GPU and 183 ms on a CPU. This performance significantly surpasses traditional high-fidelity ray tracing methods, which require approximately three orders of magnitude more time. Additionally, the model's adaptability to real-world data highlights its potential to revolutionize wireless network design and optimization, through enabling real-time creation of adaptive digital twins of real-world wireless scenarios in dynamic environments.

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arXiv2024

Colosseum as a Digital Twin: Bridging Real-World Experimentation and Wireless Network Emulation

Wireless network emulators are being increasingly used for developing and evaluating new solutions for Next Generation (NextG) wireless networks. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of the emulation process, model design, and parameter settings. To address, obviate, or minimize the impact of errors of emulation models, in this work, we apply the concept of Digital Twin (DT) to large-scale wireless systems. Specifically, we demonstrate the use of Colosseum, the world's largest wireless network emulator with hardware-in-the-loop, as a DT for NextG experimental wireless research at scale. As proof of concept, we leverage the Channel emulation scenario generator and Sounder Toolchain (CaST) to create the DT of a publicly available over-the-air indoor testbed for sub-6 GHz research, namely, Arena. Then, we validate the Colosseum DT through experimental campaigns on emulated wireless environments, including scenarios concerning cellular networks and jamming of Wi-Fi nodes, on both the real and digital systems. Our experiments show that the DT is able to provide a faithful representation of the real-world setup, obtaining an average similarity of up to 0.987 in throughput and 0.982 in Signal to Interference plus Noise Ratio (SINR).

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arXiv2022

CaST: A Toolchain for Creating and Characterizing Realistic Wireless Network Emulation Scenarios

Large-scale wireless testbeds are being increasingly used in developing and evaluating new solutions for next generation wireless networks. Among others, high-fidelity FPGA-based emulation platforms have unique capabilities for faithfully modeling real-world wireless environments in real-time and at scale, while guaranteeing repeatability. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of the emulation process, which is often overlooked. To address this unmet need in wireless network emulator-based experiments, in this paper we present CaST, a Channel emulation generator and Sounder Toolchain for creating and characterizing realistic wireless network scenarios with high accuracy. CaST consists of (i) a framework for creating mobile wireless scenarios from ray-tracing models for FPGA-based emulation platforms, and (ii) a containerized Software Defined Radio-based channel sounder to precisely characterize the emulated channels. We demonstrate the use of CaST by designing, deploying and validating multi-path mobile scenarios on Colosseum, the world's largest wireless network emulator. Results show that CaST achieves <= 20 ns accuracy in sounding Channel Impulse Response tap delays, and 0.5 dB accuracy in measuring tap gains.

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arXiv2021

Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation

Colosseum is an open-access and publicly-available large-scale wireless testbed for experimental research via virtualized and softwarized waveforms and protocol stacks on a fully programmable, "white-box" platform. Through 256 state-of-the-art software-defined radios and a massive channel emulator core, Colosseum can model virtually any scenario, enabling the design, development and testing of solutions at scale in a variety of deployments and channel conditions. These Colosseum radio-frequency scenarios are reproduced through high-fidelity FPGA-based emulation with finite-impulse response filters. Filters model the taps of desired wireless channels and apply them to the signals generated by the radio nodes, faithfully mimicking the conditions of real-world wireless environments. In this paper, we introduce Colosseum as a testbed that is for the first time open to the research community. We describe the architecture of Colosseum and its experimentation and emulation capabilities. We then demonstrate the effectiveness of Colosseum for experimental research at scale through exemplary use cases including prevailing wireless technologies (e.g., cellular and Wi-Fi) in spectrum sharing and unmanned aerial vehicle scenarios. A roadmap for Colosseum future updates concludes the paper.

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