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…

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…

The paper proposes a lightweight Temporal Convolutional Network (TCN) that incorporates physical motion-aware attention mechanisms to efficiently and effectively perform WiFi CSI-based Human Activity…

The paper introduces BFIAttack, a novel attack that exploits Beamforming Feedback Information (BFI) to reconstruct a user's Channel State Information (CSI), thereby compromising Wi-Fi physical-layer s…

The paper introduces X-Stream, a new benchmark for multi-stream video understanding, and finds that current state-of-the-art MLLMs perform poorly when required to process multiple concurrent video str…

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…

Physical AI inference (batch-1 decode) is primarily memory-bandwidth-bound, but the observed latency gap between fast and slow GPUs is not solely due to memory bandwidth, as launch-side overheads beco…

The paper proposes a lightweight, self-adaptive framework using LoRA to efficiently extract and aggregate radio frequency fingerprints for robust open-set authentication in dynamic wireless environmen…

This paper presents a comprehensive survey on reconfigurable antennas for next-generation mobile networks, focusing on their potential and applications.

The paper introduces PINSIGHT, a novel methodology that rigorously assesses Wi-Fi PIN code inference attacks by separating environmental effects from typing effects, concluding that current state-of-t…

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 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 a communication-centric 6G-LLM architecture for tactical autonomous defense vehicles, demonstrating significant improvements in coordination and communication efficiency over conven…

The paper proposes a stochastic risk-aware optimization framework for covert quantum communication, significantly improving throughput and expanding feasible operating regions under realistic channel…

The paper proposes VerFU, a client-verifiable federated unlearning framework for low-altitude wireless networks that allows devices to ensure the server accurately removes their historical data contri…

The paper proposes ExAI5G, a logic-based explainable AI framework that integrates a Transformer-based IDS with XAI techniques to provide highly accurate and transparent intrusion detection for 5G netw…

The paper introduces 5WBENCH, a new benchmark for causal unlearning, and proposes MAAT, a novel three-phase framework that achieves high forgetting and high retention specifically on complex 'Why'-typ…

The paper introduces Quantum Tunneling-Aware Machine Learning (QTAML) and a compensation algorithm (TAC) that accurately models and compensates for quantum tunneling errors in AI inference, achieving…

The paper systematically analyzes the benefits and limits of Attention-FFN Disaggregation (AFD) for Mixture-of-Experts (MoE) LLM serving, demonstrating that AFD is crucial for achieving high throughpu…

This paper demonstrates that Large Language Models (LLMs) can serve as accurate and selective surrogates for costly GPU kernel performance measurements, significantly expanding the search space for op…