arXiv subjects

Yarui Zhang

Publications and source records attributed to Yarui Zhang.

5 recordsLinked to original sources

arXiv2026

B-ExposNet: Bayesian Deep Learning for EMF Exposure Mapping with Uncertainty Quantification

Electromagnetic field exposure in urban environments is highly variable, and conventional exposure maps provide a deterministic estimate, without considering its uncertainty. To address the gap, this paper proposes B-ExposNet, a Bayesian neural network for spatial exposure estimation with uncertainty quantification. B-ExposNet extends ExposNet by treating the weights of regression head as random variables, inferred by variational inference, so that the model provides probabilistic estimates from posterior predictive distribution. The method is evaluated on drive-test measurement campaigns conducted in three French cities, and compared with conventional method such as universal Kriging, in both predictive accuracy and the quality of the estimated uncertainty. On the test region, B-ExposNet achieves a mean absolute percentage error of 28.19\% and a continuous ranked probability score of 0.198~V/m, improving on canonical methods in both accuracy and uncertainty quantification.

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arXiv2026

UrbanEMF: City-Scale EMF Mapping over a Continuous Urban Area with Real-World Base-Station Deployment

Electromagnetic Fields (EMF) mapping is essential for wireless propagation modeling and are fundamental to a wide range of applications, including spectrum awareness, network planning, and integrated sensing and communication (ISAC). However, existing datasets are often limited to spatially separated 2D scenes without real base station (BS) information. To address this gap, we present UrbanEMF, a city-scale EMF mapping dataset constructed from real world urban geometry and physical BS deployments using ray tracing. Unlike conventional single scene-based datasets, UrbanEMF preserves continuous city-scale urban coverage and realistic transmitter location information. It further extends traditional 2D maps by incorporating multiple receiver heights. In addition, both aggregated received signal strength (RSS) maps and path loss maps are provided to characterize complementary aspects of the radio environment. This work provides a realistic and flexible benchmark for developing and evaluating learning-based methods in large-scale urban wireless environments. The code for this work is available at: https://github.com/lemonstudy/UrbanEMF

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arXiv2026

From Network Automation to Trustworthy Autonomous Networking in the LLM Era: A Network Control Intelligence Perspective

Since the inception of modern communication networks, the quest for operations automation has never ceased. Yet the evolution of network automation is difficult to characterize with a single maturity ladder. Throughout this history, network control systems have expanded their capabilities for observation, decision support, routine execution, and operator interaction, but these capabilities have not advanced uniformly. Such uneven progress makes the degree of automation an unreliable proxy for trustworthy network-side actuation. The unresolved question is not simply how much automation a system provides, but under what conditions it can be entrusted to change the network state. This paper examines that question through Network Control Intelligence (NCI), a five-axis framework spanning Decision Logic, Adaptability, Knowledge, Control Delegation, and Interface. We use NCI to organize the evolution of network-control systems into three eras: rule-based and scripted automation, programmable and data-driven control, and Large Language Model (LLM)-enabled network operations. Viewed through this framework, the three eras reveal a persistent asymmetry. None of these gains, however, automatically determines when network control should be trusted to change the network state. We frame trustworthy autonomy as a governed alignment between what a system can infer, what it can verify, and what it is authorized to execute. On that basis, the paper develops a reference architecture that separates proposal generation from governed execution, identifies recurring integration patterns for LLM-enabled operations, and derives a research agenda for higher network autonomy under explicit assurance, safety, and governance constraints.

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arXiv2026

Phy2-ExposNet: A Physics-Informed Neural Network for EMF Exposure Mapping in Complex Urban Environments

Accurate electromagnetic field (EMF) exposure mapping is critical for wireless network planning, environmental monitoring, and the deployment of next generation communication systems. The mapping results can be converted into the form of a radio map, a key technology in digital twin communication systems, used to describe the wireless signal propagation characteristics at every location in a specific area. Existing deep learning approaches treat propagation estimation as a pure regression problem and do not enforce physical consistency in the predicted fields. In this paper, we propose Phy2-ExposNet, a novel neural network framework that decouples exposure mapping into a physics-informed estimation stage and a transformer-based residual refinement stage. It first estimates the fields under two physical constraints and then refines the resulting exposure map by capturing long range interactions and complex spatial propagation patterns. Experiments demonstrate that the proposed method achieves lower estimation error while significantly reducing model complexity compared to existing approaches. It achieves around 15% relative error reduction over strong baselines, while using over 80% fewer parameters than conventional physics-informed models. Ablation results further reveal that the physics-informed design is crucial for capturing complex propagation effects, particularly in boundary and shadow regions.

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arXiv2025

ExposNet: A Deep Learning Framework for EMF Exposure Prediction in Complex Urban Environments

The prediction of the electric field (E-field) plays a crucial role in monitoring radiofrequency electromagnetic field (RF-EMF) exposure induced by cellular networks. In this paper, a deep learning framework is proposed to predict E-field levels in complex urban environments. First, the measurement campaign and publicly accessible databases used to construct the training dataset are introduced, with a detailed explanation provided on how these datasets are formulated and integrated to enhance their suitability for Convolutional Neural Networks (CNNs)-based models. Then, the proposed model, ExposNet, is presented, and its network architecture and workflow are thoroughly explained. Two variations of the network structure are proposed, and extensive experimental analyses are conducted, demonstrating that ExposNet achieves good prediction accuracy with both configurations. Furthermore, the generalization capability of the model is evaluated. The overall results indicate that, despite being trained and tested on real-world measurements, the model performs well and achieves better accuracy compared to previous studies.

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