153 papers
This paper introduces the IW-DKF, a deep Kalman filter framework enhanced by importance sampling, which demonstrates improved generative modeling performance and more accurate state and parameter estimation for non-linear physics-based models compared to standard variational approaches.
This paper proposes a novel spatial IDFT technique to eliminate beam squint in massive phased arrays by counteracting inherent array-induced distortions, while also analyzing the underlying causes of squint and demonstrating how OFDM modulation can mitigate resulting signal quality issues.
This paper proposes an approximate expectation-maximization algorithm that directly reconstructs 3-D molecular structures from low signal-to-noise ratio cryo-EM micrographs, bypassing the traditional and often failing step of locating individual projection images.
This paper proposes a dual-fed pinching antenna system (DF-PAS) that mitigates in-waveguide attenuation by employing two feed points per waveguide for dynamic selection, deriving closed-form performance metrics and an efficient optimization framework to demonstrate its superior data rates over conventional single-fed designs in both single- and multi-waveguide scenarios.
This paper presents a fully open-source design and simulation of an energy-efficient analog 8-PAM demapper implemented in IHP SG13G2 SiGe BiCMOS technology using MOSFETs, achieving 0.33 pJ/bit at 1 Gbit/s.
This paper identifies and formalizes a new "label hijacking" vulnerability in track-consensus-based distributed multi-target tracking systems, where adversaries can exploit spoofed tracks to corrupt target identities across the network, thereby highlighting the critical need for enhanced robustness at the consensus layer.
This paper proposes a method to reduce the pass-band droop's dependency on the decimation factor in -th order comb decimators by cascading a symmetric 3-tap FIR filter with coefficients determined solely by the filter order .
This study proposes and validates a maximum-based Delay-Doppler Map noise floor strategy with a two-tier verification framework for detecting GNSS interference using CYGNSS LEO satellite data, demonstrating superior sensitivity and reliability compared to existing methods in both controlled jamming tests and persistent interference regions.
This paper proposes a Deep Learning-based Conformal Bayes Filter (DCBF) that integrates conformal quantile regression with Bayesian filtering to overcome the model mismatch of traditional Kalman filters and the overconfidence of deep learning methods, thereby delivering reliable MIMO channel predictions with calibrated uncertainty.
This paper establishes a unified framework linking block-level energy statistics to low-frequency intensity-fluctuation spectra, deriving a semi-analytical model and design rules to optimize constellation shaping parameters for mitigating nonlinear interference in high-capacity coherent fiber links.
This paper proposes a tri-hybrid beamforming architecture for uplink multi-user MIMO systems using segmented waveguide-enabled pinching antennas (SWANs), optimizing digital, analog, and pinching components in both fully-connected and interleaved partially-connected structures to achieve superior performance and energy efficiency compared to conventional hybrid and pinching-antenna systems.
This paper bridges the gap between discrete-time models and practical implementation of Affine Frequency Division Multiplexing (AFDM) by establishing a continuous-time analytical framework that derives fundamental bounds, analyzes spectral characteristics under realistic pulse-shaping, and evaluates performance sensitivity to hardware impairments.
The paper proposes ACCOR, an attention-enhanced complex-valued contrastive learning framework that achieves state-of-the-art accuracy in occluded object classification using mmWave radar IQ signals across 64 GHz and 67 GHz frequencies.
This paper proposes Open-RFNet, a multi-domain supervised contrastive learning framework enhanced by an improved generative OpenMax algorithm, to achieve high-accuracy open-set recognition of unauthorized UAVs in 5G-Advanced networks, outperforming existing benchmarks with 95.12% closed-set and 96.08% open-set accuracy across 25 UAV types.
This paper proposes a series-based analytic continuation strategy using Padé approximation on the cumulant expansion of the logarithmic Laplace transform to accurately pre-distort input sequences, thereby revitalizing autoregressive processes for the efficient and precise simulation of non-Gaussian radar clutter.
This white paper proposes a structured reduced-search enumeration algorithm that achieves near-maximum-likelihood hard-decision performance in high-order MIMO systems with linear complexity, demonstrating through simulations that specific list sizes relative to constellation size effectively match full ML results even under challenging channel conditions.
This paper proposes a conditional variational autoencoder (CVAE) that efficiently and accurately reconstructs shock acceleration time series from shock response spectrum curves, overcoming the computational limitations and basis function constraints of traditional iterative optimization methods.
This paper introduces VoxKnesset, a large-scale open-access dataset of 2,300 hours of longitudinal Hebrew parliamentary speech spanning 2009–2025, which is used to benchmark and demonstrate the challenges of speaker verification and age prediction over time, revealing significant performance degradation in standard models as speakers age.
This study demonstrates that integrating cellular bioelectrical impedance signatures with supervised machine learning, particularly Random Forest, enables highly accurate (~90%) prediction of cellular malignancy, offering a promising foundation for future real-time diagnostic tools.
Wall-Street is a vehicle-mounted intelligent surface that enhances mmWave connectivity for high-speed users by steering signals into the vehicle, enabling seamless neighbor-cell searches, and facilitating reliable make-before-break handovers, resulting in significant throughput improvements and latency reductions compared to standard schemes.