Sep 2026· International Symposium ELMAR· pp. 209-213· 0 citations· 11 references
Abstract
Accurate cell-specific signal strength maps are essential for coverage analysis and interference assessment in dense urban networks. However, drive-test measurements are typically spatially sparse, leaving many street segments unobserved. This paper proposes a physics-informed hybrid model for street-level pathloss prediction in dense urban environments. We employ a calibrated antenna and pathloss model as a physical mean function, while graph-based Gaussian Process Regression models residual shadowing along the street topology. Using measurements from 107 cells, the approach transforms unreliable planning data—starting with an initial RMSE of over 17 dB—into precise radio footprints. The proposed model achieves an overall MAE of 3.6 dB and an LOO-MAE of 3.2 dB. The results indicate that combining physical calibration with topology-aware residual learning produces plausible, generalized cell footprints even from sparse measurement data.
Radiomap prediction provides a high-resolution spatial representation of channel metrics that is essential for network planning, interference management, and resource allocation. Empirical and statistical models lack site specificity, whereas deterministic ray tracing (RT) is too computationally expensive for large-sca...
Fang-Zhou Ye, Guo-Qing Li, Jin-Yan Yu et al.· International Symposium on W...· 0 citations
With the gaining prominence of aerial mobility applications, their success depends on the seamless integration of terrestrial and non-terrestrial network connectivity. However, providing reliable connectivity from terrestrial telecommunication networks remains challenging due to multi-cell interference from base statio...
G. Reddy, Kürşat Tekbıyık, Bryton J. Petersen et al.· 0 citations
A residual-guided hybrid stochastic modeling framework that adjusts the pseudo-range observations to reduce the positioning loss without requiring true pseudo-range errors, which are difficult to obtain in real-world scenarios is proposed.
Juan Yin, Wen-Qiang Li, Rui-Chang Fan et al.· Italian National Conference...· 0 citations
Accurate channel modelling is a key prerequisite for planning and improvement of wireless communication networks. Obtaining a precise channel model is therefore a pivotal task for telecommunication engineers. One method that has gained in popularity in recent years is ray-tracing. Given the widespread use, it is import...
Kamil Chmela, P. Svoboda, W. Wiedner et al.· International Symposium ELMA...· 0 citations
Low-power wide-area networks, particularly Long-Range (LoRa), are important enablers of large-scale internet of things deployments in urban environments. However, inconsistencies in measurement methodologies have led to limitations in the accuracy and reproducibility of path loss models. This work presents a large-scal...
A novel framework for generating high-fidelity channels by integrating site-specific propagation parameters predicted via machine learning (ML) using a Residual Network to predict path loss and delay spread from spatial features and uses these values to appropriately scale and correct the cluster powers in the hybrid m...
