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.

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).

The extremely high data rates provided by communications in the millimeter-length (mmWave) frequency bands can help address the unprecedented demands of next-generation wireless communications. However, atmospheric attenuation and high propagation loss severely limit the coverage of mmWave networks. To overcome these challenges, multi-input-multi-output (MIMO) provides beamforming capabilities and high-gain steerable antennas to expand communication coverage at mmWave frequencies. The main contribution of this paper is the performance evaluation of mmWave communications on top of the recently released NR standard for 5G cellular networks. Furthermore, we compare the performance of NR with the 4G long-term evolution (LTE) standard on a highly realistic campus environment. We consider physical layer constraints such as transmit power, ambient noise, receiver noise figure, and practical antenna gain in both cases, and examine bitrate and area coverage as the criteria to benchmark the performance. We also show the impact of MIMO technology to improve the performance of the 5G NR cellular network. Our evaluation demonstrates that 5G NR provides on average 6.7 times bitrate improvement without remarkable coverage degradation.

We make the connection between next generation wireless standards (5G) and vehicular communication systems. We advocate the importance of transmissions in the millimeter wave band as the only ones capable to provide the Gbit/s data rates required for raw sensor data exchange among vehicles. In this context, our paper describes methods for deriving channel propagation models via ray-tracing simulations for mmWave transmissions with applications to vehicle-to-everything (V2X) communications. It also addresses aspects related to blockage modeling, the effects of diffuse scattering and multipath fading in urban scenarios.