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

The paper proposes a multi-resolution end-to-end deep neural network for autonomous driving that dynamically adjusts input resolution to optimize the critical tradeoff between prediction accuracy and…

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

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

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 theoretically grounded adversarial multi-task learning framework (AMTIDIN) that significantly improves joint interference detection, modulation identification, and interference id…

This paper proposes an improved CNN-LSTM model for IoT intrusion detection, achieving high accuracy by combining spatial and temporal feature learning from network traffic.

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 introduces a Spatiotemporal-Aware Fault Injection (STAFI) framework to efficiently locate and time critical bit-flip vulnerabilities in DNNs used for ADAS, significantly improving fault dete…

The paper proposes a pose-conditioned, permutation-equivariant denoiser to accurately reconstruct work zone geometry using noisy Ultra-Wideband (UWB) range data from connected and autonomous vehicles…

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 DAIRE, a lightweight AI model, for highly efficient, real-time detection and classification of various cyberattacks targeting the vulnerable Controller Area Network (CAN) in the Int…

ReasonLight is a multimodal foundation model-enhanced RL framework that enables zero-shot traffic signal control by semantically refining RL-proposed actions using heterogeneous sensor and camera data…

This paper analyzes two novel, symbol-agnostic attacks—signal multiplication and negative group delay (NGD) filtering—that compromise cross-correlation-based Time-of-Arrival (ToA) estimation in narrow…