arXiv subjects

Guanyue Qian

Publications and source records attributed to Guanyue Qian.

7 recordsLinked to original sources

arXiv2026

SPARC: Sparse Path-Aware Residual Calibrator for Wireless Ray Tracing at Upper Mid-Band

Accurate site-specific ray tracing (RT) is essential for upper mid-band network planning, yet raw RT can produce per-path multipath component (MPC) power errors on the order of 19--24~dB in cluttered indoor environments. A fixed-geometry material-sensitivity bound shows that a 30% relative-permittivity perturbation changes each surface interaction by at most 6.28~dB across the considered indoor materials. However, even MPCs with only one surface interaction exhibit a 19.2~dB mean RT--measurement bias, suggesting that missing clutter, displaced surfaces, and simplified 3D geometry dominate the per-path RT error. We propose SPARC (Sparse Path-Aware Residual Calibrator), a lightweight per-path calibration method that learns a sparse linear residual model from one completed RT simulation. SPARC uses standard RT features selected per fold by nested cross-validation, with ridge regularization and power-gated path matching; four features recur in both environments. Using measured indoor factory (InF) and indoor hotspot (InH) datasets at 6.75 and 16.95~GHz, SPARC reduces per-path power RMSE from 18.74 to 4.74~dB in InF and from 23.12 to 5.39~dB in InH. A jointly trained InF+InH model achieves 5.73~dB RMSE. When all links from one transmitter location are held out for testing, SPARC achieves 4.99~dB RMSE in InF and 5.85~dB RMSE in InH. SPARC therefore provides a practical post-processing calibration layer for site-specific per-path power prediction without ray-tracer modification or additional RT runs.

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arXiv2026

The Anatomy of RF Chains: Metrics, Measures, and Operating Efficiency

In 1958, Haus and Adler [1] introduced the concept of noise measure. Noise measure is a single quantitative metric that provides a comprehensive basis for comparing devices (individual circuits or outcomes of optimization iterations) in terms of their contribution to overall system noise by incorporating both noise factor and available power gain. Unlike noise factor alone, which reflects how much a device degrades the signal-to-noise ratio, noise measure captures the trade-off between noise and gain, making it a system-aware metric. This distinction is especially important when comparing devices in multistage systems, where both parameters jointly influence the overall system noise. Building on Haus and Adler's work, this article aims to advance RF system design by extending traditional device metrics, such as noise factor and noise measure, with new system-aware metrics: linearity, dynamic range, power efficiency, and waste measures. These new measures are interpretable, computable, and cascadable, making them well-suited for comparing the impact of individual devices or tracking the convergence of circuit design iterations on overall system-level performance. Additionally, a new metric--operating efficiency--is introduced, which unifies power efficiency, dynamic range, and data rate by incorporating signal statistics and variability in communication circuits and systems. Operating efficiency enables robust evaluation of devices under realistic and transient operating conditions, including interference, modulated signals, and adaptive modulation schemes.

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arXiv2026

Robust Hybrid Beamforming with Liquid Crystal Antennas and Liquid Neural Networks

Sub-terahertz (sub-THz) multi-user multiple-input multiple-output (MU-MIMO) systems unlock immense bandwidth for 6G wireless communications. However, practical deployment of wireless systems in sub-THz bands faces critical challenges such as increased atmospheric absorption, reduced channel coherence time due to increased Doppler spread at higher carrier frequencies, and hardware bottlenecks as low-loss sub-THz phase shifters are difficult to realize. To overcome the hardware and channel estimation challenges of sub-THz systems, this paper proposes a hybrid beamforming (BF) framework that integrates reconfigurable liquid crystal (LC) antennas with a liquid neural network (LNN) for transmitter. Specifically, we employ an LC antenna as the analog BF stage of a hybrid BF architecture, exploiting its voltage-driven permittivity tunability to achieve high-gain beam steering without the need for lossy phase shifters. For digital BF, we utilize an ordinary differential equations-defined LNN to learn temporal channel dynamics, and use a manifold optimization technique to compress the search space. We validated the proposed method on simulated site-specific 108 GHz ray-tracing channels in an urban scenario using NYURay, a ray-tracing simulator validated against 142 GHz propagation measurements. The 108 GHz carrier frequency matches the operating band of the LC antenna hardware. The proposed method achieves an 88.6\% spectral efficiency (SE) gain and higher robustness to imperfect channel estimation compared to the learning-aided gradient descent and gated recurrent unit machine learning baselines, and 1.9 times higher SE than the 3GPP TR~38.901 standard antenna model, highlighting the potential of LC-based hardware for sub-THz communications.

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arXiv2026

NYUSIM: A Roadmap to AI-Enabled Statistical Channel Modeling and Simulation

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.

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arXiv2026

HoRAMA: Holistic Reconstruction with Automated Material Assignment for Ray Tracing using NYURay

Next-generation wireless networks at upper mid-band and millimeter-wave frequencies require accurate site-specific deterministic channel propagation prediction. Wireless ray tracing (RT) provides site-specific predictions but demands high-fidelity three-dimensional (3D) environment models with material properties. Manual 3D model reconstruction achieves high accuracy but requires weeks of expert effort, creating scalability bottlenecks for large environment reconstruction. Traditional vision-based 3D reconstruction methods lack RT compatibility due to geometrically defective meshes and missing material properties. This paper presents Holistic Reconstruction with Automated Material Assignment (HoRAMA) for wireless propagation prediction using NYURay. HoRAMA generates RT-compatible 3D models from RGB video readily captured using a smartphone or low-cost portable camera, by integrating MASt3R-SLAM dense point cloud generation with vision language model-assisted material assignment. The HoRAMA 3D reconstruction method is verified by comparing NYURay RT predictions, using both manually created and HoRAMA-generated 3D models, against field measurements at 6.75 GHz and 16.95 GHz across 12 TX-RX locations in a 700 square meter factory. HoRAMA ray tracing predictions achieve a 2.28 dB RMSE for matched multipath component (MPC) power predictions, comparable to the manually created 3D model baseline (2.18 dB), while reducing 3D reconstruction time from two months to 16 hours. HoRAMA enables scalable wireless digital twin creation for RT network planning, infrastructure deployment, and beam management in 5G/6G systems, as well as eventual real-time implementation at the edge.

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arXiv2026

Site-Specific Location Calibration and Validation of Ray-Tracing Simulator NYURay at Upper Mid-Band Frequencies

Ray-tracing (RT) simulators are essential for wireless digital twins, enabling accurate site-specific radio channel prediction for next-generation wireless systems. Yet, RT simulation accuracy is often limited by insufficient measurement data and a lack of systematic validation. This paper presents site-specific location calibration and validation of NYURay, NYU's in-house ray tracer, at upper mid-band frequencies (6.75 GHz and 16.95 GHz). We propose a location calibration algorithm that corrects GPS-induced position errors by optimizing transmitter-receiver (T-R) locations to align simulated and measured power delay profiles, improving T-R location accuracy by 42.3% for line-of-sight (LOS) and 13.5% for non-line-of-sight (NLOS) scenarios. Validation across 18 T-R locations over distances from 40 to 880 m between transmitter and receiver shows excellent RT accuracy in path loss prediction, with path loss exponent (PLE) deviations under 0.14 when comparing measured versus RT predicted power at all T-R locations. While RT underestimates measured delay spread and angular spreads, their cumulative distributions remain statistically similar. The validated NYURay advances RT validation and provides reliable channel statistics for 6G deployment.

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arXiv2025

Standardized Machine-Readable Point-Data Format for Consolidating Wireless Propagation Across Environments, Frequencies, and Institutions

The necessity of new spectrum for 6G has intensified global interest in radio propagation measurements across emerging frequency bands, use cases, and antenna types. These measurements are vital for understanding radio channel properties in diverse environments, and involve time-consuming and expensive campaigns. A major challenge for the effective utilization of propagation measurement data has been the lack of a standardized format for reporting and archiving results. Although organizations such as NIST, NGA, and 3GPP have made commendable efforts for data pooling, a unified machine-readable data format for consolidating measurements across different institutions and frequencies remains a missing piece in advancing global standardization efforts. This paper introduces a standardized point-data format for radio propagation measurements and demonstrates how institutions may merge disparate campaigns into a common format. This data format, alongside an environmental map and a measurement summary metadata table, enables integration of data from disparate sources by using a structured representation of key parameters. Here, we show the efficacy of the point-data format standard using data gathered from two independent sub-THz urban microcell (UMi) campaigns: 142 GHz measurements at New York University (NYU) and 145 GHz measurements at the University of Southern California (USC). A joint path loss analysis using the close-in path loss model (1 m ref. distance) yields a refined estimate of the path loss exponent (PLE) employing the proposed standard to pool measurements. Other statistics such as RMS delay spread and angular spread are also determined using a joint point-data table. Adopting this simple, unified format will accelerate channel model development, build multi-institutional datasets, and feed AI/ML applications with reliable training data in a common format from many sources.

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