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.

This paper proposes a statistical framework for designing task-aware multiple-input multiple-output (MIMO) precoders in future wireless networks using statistical channel state information and trainin…

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

This paper proposes a unified deep learning framework for joint NBI cancellation and robust soft demodulation using NBI-CNet and LLR-CNet to eliminate error floors and improve performance in OFDM syst…

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.

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 AirPASS, an alternating optimization framework for over-the-air federated learning in wireless systems using a multi-waveguide pinching antenna system.

A cross-fused generative framework is proposed for low-overhead beam alignment in Uniform Planar Array systems using a decoupled channel sensing strategy and a bidirectional cross-attention encoder.

The paper proposes DRIFT, a lightweight joint channel estimation and prediction framework, to significantly reduce pilot overhead and boost spectral efficiency in power-constrained LEO Non-Terrestrial…

This paper introduces a graph-based framework for fair comparison of cellular, coordinated, and cell-free massive-MIMO systems, and derives compatible spectral efficiency expressions for uplink and do…

This paper proposes a lightweight, distributed machine learning framework for sub-centimeter indoor localization using D-MIMO in O-RAN architectures, reducing midhaul traffic by 100x while maintaining…

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 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 proposes a simplified Temporal Convolutional Network-based estimator to improve channel estimation in vehicular communication.

This paper proposes a simplified Temporal Convolutional Network-based estimator to improve channel estimation in vehicular communication.