Methods for accurate prediction of radio signal quality parameters are crucial for optimization of mobile networks, and a necessity for future autonomous driving solutions. The power-distance relation of current empirical models struggles with describing the specific local geo-statistics that influence signal quality parameters. The use of empirical models commonly results in an over- or under-estimation of the signal quality parameters and require additional calibration studies.In this paper, we present a novel model-aided deep learning approach for path loss prediction, which implicitly extracts radio propagation characteristics from top-view geographical images of the receiver location. In a comprehensive evaluation campaign, we apply the proposed method on an extensive real-world data set consisting of five different scenarios and more than 125.000 individual measurements.It is found that 1) the novel approach reduces the average prediction error by up to 53 % in comparison to ray-tracing techniques, 2) A distance of 250 -300 meters spanned by the images offer the necessary level of detail, 3) Predictions with a root-mean-squared error of \approx 6 dB is achieved across inherently different data sources.
|Title of host publication||Proceedings of 2020 IEEE Global Communications Conference|
|Number of pages||6|
|Publication date||Dec 2020|
|Publication status||Published - Dec 2020|
|Event||2020 IEEE Global Communications Conference - Virtual, Taipei, Taipei, Taiwan, Province of China|
Duration: 7 Dec 2020 → 11 Dec 2020
|Conference||2020 IEEE Global Communications Conference|
|Country||Taiwan, Province of China|
|Period||07/12/2020 → 11/12/2020|
|Sponsor||6G Office, Chunghwa Telecom Co. Ltd., Foxconn, Huawei, MediaTek|
|Series||2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT Part of the work is supported by funding provided by The Technical University of Denmark, Department of Photonics Engineering and has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Analysis”, project B4.
© 2020 IEEE.