This paper proposes a tensor-structured multi-domain channel extrapolation framework to reduce pilot overhead in upper mid-band massive MIMO systems, using a Tucker-based SFT-domain signal model and a…

This paper proposes a model for cross-band CSI reconstruction in multi-band low-altitude wireless systems using radio-frequency metadata and pilot-guided cross-attention.

The paper introduces Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) to achieve massive, structured compression of deep neural networks, demonstrating compression ratios up to 77,000x…

This paper proposes a unified RF map construction framework using physics-informed neural networks and graph neural networks, achieving high-fidelity RF map construction under sparse observations.

EigeNet introduces a geometry-informed multi-modal Transformer framework to achieve state-of-the-art few-shot novel view Room Impulse Response (RIR) prediction by effectively integrating spatial geome…

A new framework called Geo-DConv is proposed to make multi-channel speech enhancement systems adaptable to diverse microphone array geometries by leveraging microphone coordinates.

The paper proposes GUIDE, a physics-guided deep unfolding framework that enables practical, real-time cross-band channel prediction for AI-RAN by embedding wireless channel physics, significantly impr…

This paper provides explicit error bounds for the infinite-width Gaussian-process limit of random neural networks using tensor programs and quantitative convergence theory in Wasserstein distance.

This paper introduces the Distributed Truncated Spectral Transform (DTST) for Fourier Neural Operators (FNOs), achieving significant speedups in distributed computing.

This paper proposes a new definition of source in source tracing as a compositional tuple of Architecture, Training Data, and other training factors, and introduces a framework using Structured Orthon…

This paper proposes DANCE, a diffusion-based channel estimator for OFDM systems using a sparse linear inverse problem and a noise-adaptive posterior correction.

The paper proposes Domain-Aware Sharpness Minimization (DASM), a novel optimizer that enhances the robustness and generalization of voice stream steganalysis models across varying data distributions.

The paper introduces dynamic Stiefel routing, a novel method that adaptively selects specialized subspace projection filters on the Stiefel manifold to improve cross-domain EEG decoding without requir…

The Autonomous Rectified Flow framework is proposed to improve generative speech enhancement by eliminating explicit time-step conditioning and inferring denoising directions from spatial relationship…

This paper proposes a generative AI framework using a cGAN for proactive interference mitigation in ultra-dense indoor networks, achieving significant SINR gain, packet-loss reduction, and CSI oracle…

This paper proposes ConsisFormer, a compute-efficient Transformer design for wireless foundation models (WFMs) using short-term channel consistency and adaptive token aggregation.