arXiv subjects

Daniel Abraham

Publications and source records attributed to Daniel Abraham.

12 recordsLinked to original sources

arXiv2026

Map-Free Single-Anchor Position Localization Using Multipath Uncertainty at Upper Mid-Band

Future 5G-Advanced and 6G systems will exploit directional wideband channels for path-based localization, yet map-free indoor positioning remains challenging because extracted multipath components (MPCs) vary widely in localization reliability. This paper presents an uncertainty-aware map-free single-anchor localization framework using 16.95~GHz indoor directional channel measurements collected at NYU WIRELESS in Brooklyn, New York. Per-MPC angular covariance is estimated from the local consistency of neighboring pattern-de-embedded power delay profile (PDP) observations and propagated into geometric localization through weighted fusion of point-type and line-type constraints. Measurements across 20 links with TX--RX separations from 11~m to 97~m show that the proposed covariance-weighted method achieves a mean localization error of 3.86~m, a median of 2.66~m, and 70\% of links within 5~m using five retained MPCs. It outperforms the power-weighted and unweighted variants under identical point/line constraints, as well as the literature baselines. These results indicate that local directional consistency provides reliability information beyond received power alone and demonstrate a practical map-free approach for future indoor directional systems.

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arXiv2026

Omnidirectional UMi Path Loss Models for 3GPP Extension Above 100 GHz Using Extensive Empirical Data Sets from 6.75 to 142 GHz

Extending standardized urban microcell (UMi) path loss (PL) models from the upper mid-band (FR3) to sub-terahertz (sub-THz) frequencies requires measurement-based comparisons spanning a wide spectral range within a unified framework. In this paper, we derive single- and multi-frequency omnidirectional PL models for UMi environments based on extensive NYU WIRELESS measurements at 6.75, 16.95, 28, 73, and 142 GHz in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Single-frequency close-in (CI) and floating-intercept (FI) models for each band show that CI yields more stable and physically interpretable parameters, with shadow fading standard deviations within 0.98 dB of FI across all five frequencies. We then extend the multi-frequency PL analysis to the 7-24 GHz, 0.5-100 GHz, and 0.5-150 GHz frequency ranges using CI, close-in with frequency weighting (CIF), and alpha-beta-gamma (ABG) models. Across progressively wider frequency spans, CI and CIF maintain stable distance exponents that remain closely aligned with the 3rd Generation Partnership Project (3GPP) UMi interpretations, whereas ABG offers only very modest reductions in fitting error at the cost of much greater parameter sensitivity. These results support physically anchored CI/CIF formulations with a close-in free space path loss anchor point at 1 m in order to extend 3GPP-oriented UMi PL models over the entire 0.5 to 150 GHz frequency range.

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arXiv2026

Global bifurcation for steady viscous roll waves on an incline

We construct a branch of travelling periodic `roll wave' solutions to the free-boundary incompressible Navier--Stokes equations on an inclined plane in two dimensions. These solutions bifurcate from a parallel shear flow, under natural assumptions on the related Orr--Sommerfeld equation. Using techniques from analytic global bifurcation theory, we extend the local branch to a global curve of solutions. A key step of the proof is reformulating the problem, including the unknown free boundary, as an elliptic system in the sense of Agmon--Douglis--Nirenberg. Finally, we verify the hypotheses on the Orr--Sommerfeld equation for two regimes: small wavenumber and low Reynolds number.

math.AP↗

arXiv2026

Fast Voxelwise SNR Estimation for Iterative MRI Reconstructions

Purpose: To develop a fast, general-purpose framework for voxelwise noise characterization in linear and nonlinear iterative MRI reconstructions, recovering the image-domain noise variance from which SNR, $g$-factor, and related image-quality metrics are derived. The framework addresses both the intractability of closed-form formulas beyond Cartesian sampling and the long runtime of Pseudo Multiple Replica (PMR) methods. Methods: We propose PICO (Probing Image-space COvariance), an estimator that operates in the image domain by probing the image-domain noise covariance operator -- or, for nonlinear compressed-sensing reconstructions, the Jacobian of the converged solution -- with random probe images. Complex random-phase probes are shown theoretically and empirically to minimize estimator variance compared with Gaussian or real-valued alternatives. PICO was validated against analytical benchmarks and high-replica PMR references using retrospective Cartesian knee data ($R=2$), prospective non-Cartesian spiral brain phantom data ($R=2,3,4$), and compressed-sensing knee reconstructions ($R=2$). Results: In Cartesian experiments, PICO accurately reproduced analytical SENSE $g$-factor maps. In non-Cartesian spiral imaging ($R=2$), it achieved 1% estimation error in 64 s compared with 462 s for PMR (approximately 7.2x speedup), with the efficiency advantage persisting at higher acceleration. For nonlinear compressed sensing, the Jacobian-based estimator produced noise maps consistent with PMR while converging faster (52 s vs. 95 s; approximately 1.8x speedup). Conclusion: PICO provides a computationally efficient alternative to PMR for voxelwise noise and $g$-factor estimation across generalized iterative MRI reconstructions. By reusing existing reconstruction primitives, it enables voxelwise noise maps to be produced as a routine by-product of the reconstruction pipeline.

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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

Fully 3D Unrolled Magnetic Resonance Fingerprinting Reconstruction via Staged Pretraining and Implicit Gridding

Magnetic Resonance Fingerprinting (MRF) enables fast quantitative imaging, yet reconstructing high-resolution 3D data remains computationally demanding. Non-Cartesian reconstructions require repeated non-uniform FFTs, and the commonly used Locally Low Rank (LLR) prior adds computational overhead and becomes insufficient at high accelerations. Learned 3D priors could address these limitations, but training them at scale is challenging due to memory and runtime demands. We propose SPUR-iG, a fully 3D deep unrolled subspace reconstruction framework that integrates efficient data consistency with a progressive training strategy. Data consistency leverages implicit GROG, which grids non-Cartesian data onto a Cartesian grid with an implicitly learned kernel, enabling FFT-based updates with minimal artifacts. Training proceeds in three stages: (1) pretraining a denoiser with extensive data augmentation, (2) greedy per-iteration unrolled training, and (3) final fine-tuning with gradient checkpointing. Together, these stages make large-scale 3D unrolled learning feasible within a reasonable compute budget. On a large in vivo dataset with retrospective undersampling, SPUR-iG improves subspace coefficient maps quality and quantitative accuracy at 1-mm isotropic resolution compared with LLR and a hybrid 2D/3D unrolled baseline. Whole-brain reconstructions complete in under 15-seconds, with up to $\times$111 speedup for 2-minute acquisitions. Notably, $T_1$ maps with our method from 30-second scans achieve accuracy on par with or exceeding LLR reconstructions from 2-minute scans. Overall, the framework improves both accuracy and speed in large-scale 3D MRF reconstruction, enabling efficient and reliable accelerated quantitative imaging.

eess.IV↗

arXiv2025

Fast Reconstruction of Motion-Corrupted Data with Mobile-GRAPPA: Motion and dB0 Inhomogeneity Correction Leveraging Efficient GRAPPA

Advanced motion navigations now enable rapid tracking of subject motion and dB0-induced phase, but accurately incorporating this high-temporal-resolution information into SENSE (Aligned-SENSE) is often computationally prohibitive. We propose "Mobile-GRAPPA", a k-space "cleaning" approach that uses local GRAPPA operators to remove motion and dB0 related corruption so that the resulting data can be reconstructed with standard SENSE. We efficiently train a family of k-space-position-specific Mobile-GRAPPA kernels via a lightweight multilayer perceptron (MLP) and apply them across k-space to generate clean data. In experiments on highly motion-corrupted 1-mm whole-brain GRE (Tacq = 10 min; 1,620 motion/dB0 trackings) and EPTI (Tacq = 2 min; 544 trackings), Mobile-GRAPPA enabled accurate reconstruction with negligible time penalty, whereas full Aligned-SENSE was impractical (reconstruction times > 10 h for GRE and > 10 days for EPTI). These results show that Mobile-GRAPPA incorporates detailed motion and dB0 tracking into SENSE with minimal computational overhead, enabling fast, high-quality reconstructions of challenging data.

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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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arXiv2024

ACE-Net: AutofoCus-Enhanced Convolutional Network for Field Imperfection Estimation with application to high b-value spiral Diffusion MRI

Spatiotemporal magnetic field variations from B0-inhomogeneity and diffusion-encoding-induced eddy-currents can be detrimental to rapid image-encoding schemes such as spiral, EPI and 3D-cones, resulting in undesirable image artifacts. In this work, a data driven approach for automatic estimation of these field imperfections is developed by combining autofocus metrics with deep learning, and by leveraging a compact basis representation of the expected field imperfections. The method was applied to single-shot spiral diffusion MRI at high b-values where accurate estimation of B0 and eddy were obtained, resulting in high quality image reconstruction without need for additional external calibrations.

physics.med-ph↗

arXiv2024

Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction.

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arXiv2023

Sequence adaptive field-imperfection estimation (SAFE): retrospective estimation and correction of $B_1^+$ and $B_0$ inhomogeneities for enhanced MRF quantification

$B_1^+$ and $B_0$ field-inhomogeneities can significantly reduce accuracy and robustness of MRF's quantitative parameter estimates. Additional $B_1^+$ and $B_0$ calibration scans can mitigate this but add scan time and cannot be applied retrospectively to previously collected data. Here, we proposed a calibration-free sequence-adaptive deep-learning framework, to estimate and correct for $B_1^+$ and $B_0$ effects of any MRF sequence. We demonstrate its capability on arbitrary MRF sequences at 3T, where no training data were previously obtained. Such approach can be applied to any previously-acquired and future MRF-scans. The flexibility in directly applying this framework to other quantitative sequences is also highlighted.

eess.IV↗

arXiv2023

Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction

MRI data is acquired in Fourier space/k-space. Data acquisition is typically performed on a Cartesian grid in this space to enable the use of a fast Fourier transform algorithm to achieve fast and efficient reconstruction. However, it has been shown that for multiple applications, non-Cartesian data acquisition can improve the performance of MR imaging by providing fast and more efficient data acquisition, and improving motion robustness. Nonetheless, the image reconstruction process of non-Cartesian data is more involved and can be time-consuming, even through the use of efficient algorithms such as non-uniform FFT (NUFFT). Reconstruction complexity is further exacerbated when imaging in the presence of field imperfections. This work (implicit GROG) provides an efficient approach to transform the field corrupted non-Cartesian data into clean Cartesian data, to achieve simpler and faster reconstruction which should help enable non-Cartesian data sampling to be performed more widely in MRI.

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