147 papers
This paper proposes a unified Lagrangian regression framework that naturally incorporates the Discrete Cosine Transform (DCT) as a novel, computationally efficient, and convergent alternative to traditional polynomial and logistic regression methods.
This paper introduces a novel set-prediction-based framework for J-peak detection in pillow-based ballistocardiography, supported by a newly released multi-subject dataset, which outperforms traditional segmentation approaches while significantly reducing model complexity and eliminating the need for heuristic post-processing.
The paper introduces MAD, a novel multimodal affective dataset featuring synchronized physiological signals (EEG, ECG, EOG, EMG, PPG, BCG) and tri-view RGB-D facial videos from 18 participants, accompanied by a hierarchical three-level annotation framework to facilitate comprehensive research in emotion recognition and cross-modal affective analysis.
This paper proposes a unified retrieval-assisted framework for wireless localization that combines channel charting for efficient low-dimensional retrieval of reference points with a graph attention network to model inter-sample correlations, thereby achieving superior accuracy and scalability compared to existing similarity-based and learning-based methods.
This paper proposes a model-based reinforcement learning strategy to optimize relay power allocation in an underwater hybrid optical-acoustic energy harvesting network, effectively maximizing long-term secrecy performance against acoustic eavesdropping while adapting to dynamic channel conditions and battery constraints.
This paper proposes a conditioning-aware channel estimation framework for RIS-assisted upper mid-band MIMO systems that overcomes severe spatial correlation and ill-conditioning in near-field propagation by transforming the high-dimensional problem into well-conditioned subproblems through greedy column grouping and piecewise phase design, thereby achieving robust performance without relying on sparsity assumptions.
This paper analyzes the ill-posedness of CSI-based electromagnetic inverse scattering in ISAC systems by characterizing the coherence of scattering operators, proving that restricting the reconstruction to a Region of Interest (ROI) significantly improves conditioning and estimation bounds, and validating a corresponding ROI-constrained optimization framework through simulations.
This article reviews the state-of-the-art of stacked intelligent metasurfaces (SIMs) for in-wave computation in next-generation networks, highlighting their potential to jointly optimize communication, sensing, and processing while addressing current challenges in scalability, controllability, and robustness.
This paper proposes a balanced 6G waveform strategy that integrates orthogonality and non-orthogonality, identifying SC-NOFS(2D) as a superior candidate over SC-OFDM for achieving high data rates, low latency, and robust mobility while ensuring backward compatibility.
This paper characterizes the frequency response of windowed DFT-based phasor estimators used in PMUs, deriving their complex gain to explain how estimation windows distort oscillation measurements and proposing a method to recover true oscillation parameters for improved observability.
This paper extends a hardware-efficient hybrid digital-analog RIS architecture by proposing a pragmatic phase-only beam design for hierarchical codebooks and a low-complexity multiuser MIMO framework that achieves high spectral efficiency under realistic mmWave multipath channels while maintaining full compliance with 3GPP 5G NR standards.
This paper introduces a unified framework for analyzing wireless network utility regions based on the spectral radius of nonlinear mappings, offering a powerful mathematical tool to characterize feasible regions, derive tractable conditions for convexity, and optimize sum-rate maximization across cellular, cell-less, and hybrid architectures.
This paper characterizes the transition of first arrival position channel noise from heavy-tailed Cauchy to exponentially decaying distributions under nonzero drift, identifying a characteristic propagation distance that delineates diffusion-dominated and drift-dominated regimes while demonstrating that Gaussian approximations fail to capture communication potential in low-drift environments.
This paper proposes two novel unequal error protection frameworks for digital semantic communication that leverage learned bit-flip probabilities to assign heterogeneous reliability levels, demonstrating that partitioning semantic bits into short blocks with tailored channel codes significantly outperforms conventional equal-protection schemes in both task performance and transmission efficiency.
This survey provides a comprehensive overview of stacked intelligent metasurfaces (SIMs), detailing their physical principles, modeling frameworks, and hardware realizations while examining their role in enabling advanced 6G functionalities like wave-domain signal processing, integrated sensing, and cell-free networks, alongside identifying key challenges for future research.
This paper proposes a heuristic MIMO-CC delivery framework that enables asymmetric stream allocation while guaranteeing linear decodability through a newly derived criterion, thereby expanding the achievable Degrees of Freedom region beyond the limitations of existing symmetric-constrained designs.
This paper analyzes the secrecy performance of continuous-aperture array (CAPA) systems over Rayleigh fading channels, deriving theoretical expressions for secrecy rate and outage probability across single, multiple independent, and multiple collaborative eavesdropper scenarios, and demonstrating that CAPA systems outperform traditional spatially-discrete arrays by achieving higher secrecy rates, lower outage probabilities, and a diversity order equal to the spatial degrees of freedom.
This paper proposes a physically consistent signal model and an alternating optimization framework for downlink sum-rate maximization in STAR beyond-diagonal RISs with per-element amplification, achieving substantial performance gains over conventional passive systems while ensuring hardware feasibility and passivity.
This paper presents a low-cost, wireless wearable Electrical Impedance Tomography system utilizing five synchronized parallel AD5933 chips and specialized signal conditioning to achieve high-precision, real-time imaging of biological tissues such as human lungs with a signal-to-noise ratio exceeding 50 dB.
This paper introduces Multivariate Fields of Experts, a new image prior framework that generalizes existing methods using multivariate potential functions to achieve fast, interpretable, and theoretically guaranteed convergence in various inverse problems, outperforming univariate models while approaching deep learning performance with significantly fewer parameters and data.