Jul 2026· European Conference on Artificial Intelligence· pp. 1-6· 0 citations· 19 references
Abstract
Coverage map estimation is fundamental in planning wireless communication systems to enhance service quality and minimize operational costs. Although traditional deterministic propagation models offer high precision, their computational costs and processing times increase exponentially, especially in urban areas. In this study, a ResNet-based Conditional Variational Autoencoder (ResNet-CVAE) architecture is proposed for rapid and accurate coverage map generation in scenarios involving multiple diffractions. In order to generate dataset, a dynamic ray-tracing code integrating Geometric Optics (GO) and the Uniform Theory of Diffraction (UTD) was developed. By processing obstacle geometry and transmitter locations as both numerical and spatial condition information, the proposed deep learning model successfully captures abrupt signal level drops and physical shadowing effects behind obstacles. Experimental results demonstrate that the ResNet-CVAE model produces high accurate results with significantly lower computational overhead compared to traditional methods and adapts effectively to complex obstacle configurations. This approach offers significant potential for real-time analysis in network planning and base station placement processes.
A Transformer-enhanced Double Deep Q-Network (TDDQN) framework that effectively integrates a Standard Transformer Encoder with a hierarchical global-local map representation for large-scale urban AAV navigation to enable scalable operation and temporal reasoning in complex urban landscapes is proposed.
Radio maps (RMs) characterize the spatial distribution of wireless channel features and provide essential foundations for environment-aware wireless communication. Existing RM reconstruction methods mainly focus on pathloss prediction on fixed two-dimensional planes, while three-dimensional (3D) joint inference of path...
Bowen Zhu, Meng Wang, Pan Zhen et al.· IEEE Wireless Communications...· 0 citations
A deep learning-based approach is presented that optimizes environmental input construction for accurate channel path loss prediction and validate that the input feature construction improves channel prediction accuracy and provides guidance for future intelligent channel prediction.
Zhicheng Qiu, Rui-Si He, Bo Ai et al.· npj Wireless Technology· 0 citations
The proposed IQ-MobNet DoA estimation model achieves this competitive performance with substantially lower computational complexity, requiring only 0.24 million parameters and 0.42 million Floating Point Operations (FLOPs), representing a reduction of over 96% compared to the recent neural network models.
Neeraja P. Kovilakam, Bindiya T. Sambasivan, Raghu C. Variyam· Electronics· 0 citations
This work proposes Physics-Unrolled Hybrid Neural Operator (PU-HNO), a three-stage cascade that predicts high-fidelity indoor radio maps from low-fidelity ray-tracing outputs and scene priors by progressively capturing reflection, diffraction, and scattering effects, rather than treating radio maps as generic images.
Rafid Umayer Murshed, S. ur Rahman, Mingyue Tang et al.· 0 citations
Accurate wireless channel prediction in urban environments is essential for network planning and optimization, but traditional ray tracing (RT) simulations are computationally expensive. This paper presents a deep learning approach that learns from sparse RT simulations to predict received power for unseen transmitter...
Eran Greenberg, Itzik Klein· 2026 6th International Confe...· 0 citations
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