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 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 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 framework for multi-site channel charting in wireless networks using topological signal processing, enabling coherent integration of locally learned representations into a shared…

This paper proposes a physics-guided framework, PathRIR, for fast room impulse response simulation using image-source-method, preserving geometric structure while pruning acoustically insignificant pa…

This paper proposes a physical layer authentication mechanism using integrated sensing and communication (ISAC) to reconstruct environment layout, infer propagation channels, and authenticate transmit…

The paper introduces SpikeWFM, a novel hybrid architecture combining spiking neural networks (SNNs) and transformers, which significantly improves the robustness and accuracy of wireless foundation mo…

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 surveys model forensics in AI-native wireless networks, detailing key security problems and demonstrating practical workflows for verifying model authenticity and detecting malicious functi…

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 tests the impact of acoustic factors on voice control systems and introduces a Dual-Form Signal to Noise Ratio to decouple source stealth from attack efficacy.

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 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…

A statistical model is proposed for UWB propagation channels inside the human chest for implant medical sensors, providing formulas for path loss, scattering, and channel impulse response.

The paper introduces RadioMaster, a novel multi-agent system that successfully translates high-level user intents into physically viable, real-world radio signals, significantly outperforming existing…

This paper studies covert communication in a scalar Gaussian model, deriving the maximal reliably transmissible covert payload and establishing first-order optimality.

The paper proposes a channel prediction-based Physical Layer Authentication (PLA) framework using a Transformer module to maintain robust authentication accuracy against consecutive spoofing attacks i…

This paper presents a portable, battery-powered RF capture system using a HackRF One SDR, Raspberry Pi 5, GNSS receiver, and high-speed storage for recording geotagged IQ data in real-world environmen…