arXiv subjects

Haofan Lu

Publications and source records attributed to Haofan Lu.

5 recordsLinked to original sources

arXiv2026

mmDiff: A Noise-Robust Differentiable Ray-Tracing Framework for mmWave Scene Calibration and Channel Prediction

3D reconstruction techniques such as LiDAR scanning and photogrammetry have made it practical to build detailed geometric models of real-world environments. Such reconstructed models can potentially serve as the foundation for wireless digital twins and support network planning and optimization. The core challenge is that reconstructed models inevitably contain geometric artifacts such as holes and noisy surfaces, and wireless simulation is highly sensitive to such noise. To solve this problem, we propose a differentiable directional scattering model to approximate the noise-sensitive specular reflection. This approximation smoothly distributes reflected power among nearby ray directions, making the simulator inherently robust to local geometric artifacts in the reconstructed model. We prove mathematically that this approximation preserves asymptotic path-gain accuracy. Building on this idea, we propose mmDiff, an end-to-end differentiable framework for calibrating material properties from sparse mmWave measurements and predicting mmWave channels. We evaluate mmDiff on both real-world and synthetic datasets, and demonstrate its superior performance over prior methods using pure specular reflection in noisy reconstructed geometry.

cs.NI↗

arXiv2026

Deep Hierarchical Knowledge Loss for Fault Intensity Diagnosis

Fault intensity diagnosis (FID) plays a pivotal role in intelligent manufacturing while neglecting dependencies among target classes hinders its practical deployment. This paper introduces a novel and general framework with deep hierarchical knowledge loss (DHK) to achieve hierarchical consistent representation and prediction. We develop a novel hierarchical tree loss to enable a holistic mapping of same-attribute classes, leveraging tree-based positive and negative hierarchical knowledge constraints. We further design a focal hierarchical tree loss to enhance its extensibility and devise two adaptive weighting schemes based on tree height. In addition, we propose a group tree triplet loss with hierarchical dynamic margin by incorporating hierarchical group concepts and tree distance to model boundary structural knowledge across classes. The joint two losses significantly improve the recognition of subtle faults. Extensive experiments are performed on four real-world datasets from various industrial domains (three cavitation datasets from SAMSON AG and one publicly available dataset) for FID, all showing superior results and outperforming recent state-of-the-art FID methods.

eess.AS↗

arXiv2024

Enhancing IoT Communication and Localization via Smarter Antenna

The convergence of sensing and communication functionalities is poised to become a pivotal feature of the sixth-generation (6G) wireless networks. This vision represents a paradigm shift in wireless network design, moving beyond mere communication to a holistic integration of sensing and communication capabilities, thereby further narrowing the gap between the physical and digital worlds. While Internet of Things (IoT) devices are integral to future wireless networks, their current capabilities in sensing and communication are constrained by their power and resource limitations. On one hand, their restricted power budget limits their transmission power, leading to reduced communication range and data rates. On the other hand, their limited hardware and processing abilities hinder the adoption of sophisticated sensing technologies, such as direction finding and localization. In this work, we introduce Wi-Pro, a system which seamlessly integrates today's WiFi protocol with smart antenna design to enhance the communication and sensing capabilities of existing IoT devices. This plug-and-play system can be easily installed by replacing the IoT device's antenna. Wi-Pro seamlessly integrates smart antenna hardware with current WiFi protocols, utilizing their inherent features to not only enhance communication but also to enable precise localization on low-cost IoT devices. Our evaluation results demonstrate that Wi-Pro achieves up to 150\% data rate improvement, up to five times range improvement, accurate direction finding, and localization on single-chain IoT devices.

cs.NI↗

arXiv2024

NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction

We present NeWRF, a deep learning framework for predicting wireless channels. Wireless channel prediction is a long-standing problem in the wireless community and is a key technology for improving the coverage of wireless network deployments. Today, a wireless deployment is evaluated by a site survey which is a cumbersome process requiring an experienced engineer to perform extensive channel measurements. To reduce the cost of site surveys, we develop NeWRF, which is based on recent advances in Neural Radiance Fields (NeRF). NeWRF trains a neural network model with a sparse set of channel measurements, and predicts the wireless channel accurately at any location in the site. We introduce a series of techniques that integrate wireless propagation properties into the NeRF framework to account for the fundamental differences between the behavior of light and wireless signals. We conduct extensive evaluations of our framework and show that our approach can accurately predict channels at unvisited locations with significantly lower measurement density than prior state-of-the-art

cs.NI↗

arXiv2022

WiFi Physical Layer Stays Awake and Responds When it Should Not

WiFi communication should be possible only between devices inside the same network. However, we find that all existing WiFi devices send back acknowledgments (ACK) to even fake packets received from unauthorized WiFi devices outside of their network. Moreover, we find that an unauthorized device can manipulate the power-saving mechanism of WiFi radios and keep them continuously awake by sending specific fake beacon frames to them. Our evaluation of over 5,000 devices from 186 vendors confirms that these are widespread issues. We believe these loopholes cannot be prevented, and hence they create privacy and security concerns. Finally, to show the importance of these issues and their consequences, we implement and demonstrate two attacks where an adversary performs battery drain and WiFi sensing attacks just using a tiny WiFi module which costs less than ten dollars.

cs.NI↗