We propose a transfer learning enhanced hybrid model for robust reference signal received power (RSRP) prediction. The hybrid model comprises an expected RSRP estimation based on transmit power, 3-D antenna gain models, path loss, and a deep learning (DL) for predicting an error from ground-truth measurement. The DL architecture consists of regression neural network (NN) and convolutional neural network (CNN). Besides cell site configuration and the long-term evolution (LTE) measurement report from user equipments (UEs), the expected RSRP and geospatial data e.g. building percentage and clutter index are considered. Since trained model may not perform well in new environment, it requires tedious work and long time to collect data at a new cell site. Therefore, we use transfer learning (TL) to apply the trained model to the other areas, which have differences in environment information and antenna configurations, by transferring the knowledge acquired from trained model. The results of the trained area show that root mean square error (RMSE) and mean absolute error (MAE) are approximately 2.92 and 2.01, respectively. For the other area, TL have improved MAE approximately 1 to 2.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Learning Aided Robust RSRP Prediction in Cellular Networks




    Erscheinungsdatum :

    07.10.2024


    Format / Umfang :

    1103802 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep Learning for Fast Beam Tracking using RSRP in Millimeter Wave MIMO Systems

    Zhang, Jiankun / Wang, Hao / Du, Guanglong et al. | IEEE | 2022


    Comparison of Large-Scale Fading Models with RSRP Measurements

    Fastenbauer, Agnes / Eller, Lukas / Svoboda, Philipp et al. | IEEE | 2024



    Deep Learning-based Multi-Connectivity Optimization in Cellular Networks

    Hernandez-Carlon, J. J. / Perez-Romero, J. / Sallent, O. et al. | IEEE | 2022


    Adaptive Deployment of UAV-Aided Networks Based on Hybrid Deep Reinforcement Learning

    Ma, Xiaoyong / Hu, Shuting / Zhou, Danyang et al. | IEEE | 2020