Autors: Tonchev, K., Ivanov, A. S., Neshov, N. N., Manolova, A. H., Poulkov, V. K.
Title: Learning Graph Convolutional Neural Networks to Predict Radio Environment Maps
Keywords: Graph convolutional neural networks; Radio Environment Maps

Abstract: One of the promising 5G advancements is the implementation of Ultra Dense Networks (UDN) opening possibilities for the implementation of new applications and services. However, the densification of Access Points (APs) leads to an increase in the interchannel interference, more complicated and inefficient spectrum management and utilization, and in the case of private networks the possibly for severe Quality of Service (QoS) degradation. One of the potential solutions is the implementation and utilization of Radio Environment Maps (REM) for APs location planning and spectrum and resource allocation. Building detailed REMs is a challenging task as the measurement of the signal strength in a big number of points in a given space is tedious and, in some cases, a challenging and even an impossible task.

References

    Issue

    in Proceedings of International Symposium on Wireless Personal Multimedia Communications (WPMC), Herning, Denmark, 30 October-02 November 2022, pp. 392-395, 2022, Denmark, IEEE Computer Society, DOI 10.1109/WPMC55625.2022.10014842

    Copyright IEEE

    Цитирания (Citation/s):
    1. Chaves-Villota, A., Viteri-Mera, C.A., "DeepREM: Deep-Learning-Based Radio Environment Map Estimation From Sparse Measurements", IEEE Access, vol. 11, pp. 48697-48714, 2023, DOI: 10.1109/ACCESS.2023.3277248. - 2023 - в издания, индексирани в Scopus или Web of Science
    2. Tan, Z., Xiao, L., Tang, X., Zhao, M., Li, Y., "A FL-Based Radio Map Reconstruction Approach for UAV-Aided Wireless Networks", Electronics (Switzerland), vol. 12, no. 13, 2023, DOI: 10.3390/electronics12132817. - 2023 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science