Integrating artificial intelligence (AI) into wireless channel modeling requires large, accurate, and physically consistent datasets derived from real measurements. Such datasets are essential for training and validating models that learn spatio-temporal channel behavior across frequencies and environments. NYUSIM, introduced by NYU WIRELESS in 2016, generates realistic spatio-temporal channel data using extensive outdoor and indoor measurements between 28 and 142 GHz. To improve scalability and support 6G research, we migrated the complete NYUSIM framework from MATLAB to Python, and are incorporating new statistical model generation capabilities from extensive field measurements in the new 6G upper mid-band spectrum at 6.75 GHz (FR1(C)) and 16.95 GHz (FR3) [1]. The NYUSIM Python also incorporates a 3D antenna data format, referred to as Ant3D, which is a standardized, full-sphere format for defining canonical, commercial, or measured antenna patterns for any statistical or site-specific ray tracing modeling tool. Migration from MATLAB to Python was rigorously validated through Kolmogorov-Smirnov (K-S) tests, moment analysis, and end-to-end testing with unified randomness control, confirming statistical consistency and reproduction of spatio-temporal channel statistics, including spatial consistency with the open-source MATLAB NYUSIM v4.0 implementation. The NYUSIM Python version is designed to integrate with modern AI workflows and enable large-scale parallel data generation, establishing a robust, verified, and extensible foundation for future AI-enabled channel modeling.
Reliable channel models form the foundation for the design, test, and standardization of wireless communication systems. Over the past decade, the demand for accurate, measurementbased channel modeling has increased as mobile networks evolve toward higher frequencies, wider bandwidths, and greater spatial complexity in antenna radiation patterns. The NYUSIM channel simulator, introduced by NYU WIRELESS in 2016 [2]- [4], has become a global reference platform for studying millimeter-wave (mmWave), sub-Terahertz (sub-THz), and now upper mid-band (FR3) propagation environments. Built on more than a decade of outdoor and indoor measurement campaigns, spanning 28 to 142 GHz, NYUSIM implements the statistical spatial channel model (SSCM) using the time cluster spatial lobe (TCSL) framework [5], [6]. NYUSIM captures the physical behavior of multipath propagation, reproducing large-scale path loss (PL), multipath delay, angular spreads, and spatio-temporal power distributions observed in real measurements. Since its public release, NYUSIM has become one of the most widely used open-source propagation tools, downloaded over 100,000 times, cited in more than 3,300 publications (as of 2024), and incorporated into network-level simulators such as ns-3 [7], [8].
The evolution of NYUSIM, beyond version 4.0 as presented here, describes three major advancements: (1) extension to FR3 frequencies, (2) inclusion of realistic three-dimensional (3D) antenna pattern modeling, and (3) complete conversion of the MATLAB code base into a modular and open Python framework with clearly separated functional components. The FR3 band (7-24 GHz) represents the “upper mid-band” of the spectrum expected to support global 6G deployment [1]. Recent actions by the ITU, NTIA, FCC, and WRC-23 have highlighted specific FR3 sub-bands (e.g., 7.125-8.4 GHz, 14.8-15.35 GHz) as strong candidates for future mobile allocations [1]. Emerging use cases in 6G and integrated sensing and communication (ISAC) applications further increase the need for accurate, measurement-based FR3 channel models [9]. Previous upper mid-band work [10], [11] reported indoor line-of-sight (LOS) and non-line-of-sight (NLOS) path-loss exponents and delay spreads at selected frequencies (e.g., 6-14 GHz), but datasets were limited in bandwidth, environment, or angular coverage. In contrast, NYU WIRELESS conducted the world’s first comprehensive measurement campaigns pairing 6.75 GHz (FR1(C)), and 16.95 GHz (FR3) [1], [12]- [14].
Accurate statistical channel modeling in FR1(C) and FR3 requires realistic embedded antenna patterns [15], [16]. Here, we develop a 3D antenna data format, referred to as Ant3D. Ant3D is a standardized, full-sphere format for defining canonical, commercial, or measured antenna patterns for any statistical or site-specific (e.g., ray-tracing) modeling tools. Each antenna pattern is represented as a gain matrix over azimuth and elevation with optional frequency components, allowing directional and polarization effects to be incorporated into simulated spatial channel impulse responses. Ant3D extends NYUSIM beyond the canonical uniform linear array (ULA), enabling channel simulations with realistic array geometries, and sidelobe structures [17]- [19]. NYUSIM 4.0 is ported from MATLAB to Python to provide a reliable, transparent AI-native channel simulator derived from gold-standard measurement data, and it can learn as new propagation data becomes available. The migration to NYUSIM Confidential Python provides a modular, scalable software architecture to support AI workflows for generative and discriminative channel models, faithfully reproduces real-world channel spatiotemporal sample functions, while allowing future measurements to be seamlessly integrated and learned within this platform [20]- [22].
Recent advances in generative and discriminative AI have motivated a paradigm shift from purely statistical channel modeling to data-driven wireless channel synthesis. AI-driven modeling uses measured datasets to learn complex, and nonlinear mappings between environmental variables (frequency, scenario, geometry) and channel characteristics (path loss, delay spread, angular spread, cluster dynamics) [23], [24]. Generative AI models such as diffusion models, variational autoencoders (VAEs), and generative adversarial networks (GANs) are capable of learning the joint probability distribution of channel parameters [23]. Generative AI allows for realistic synthesis of unseen propagation environments by sampling from measurementbased statistical distributions that capture the spatio-temporal characteristics of real channels. Discriminative models, such as deep neural networks and random forests, are capable of predicting large-scale and small-scale parameters or beamforming statistics from input conditions such as frequency, antenna geometry, and mobility [24]. The approaches in [23], [24] are increasingly being investigated for 6G digital twins, mapaware simulat
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