A novel beam-domain channel estimation method for mmWave MIMO is proposed using sub-6 GHz beam-domain information for improved accuracy.

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.

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

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 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 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 a framework for on-site calibration of Reconfigurable Intelligent Surface (RIS) beam response models to reduce positioning error floor.

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…

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 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 analyzes the effects of calibration errors on downlink beamforming in a repeater-assisted massive MIMO system and derives analytical expressions for the downlink spectral efficiency.