Wongphatcharatham, T., Phakphisut, W., Jaruvitayakovit, T., Boonkajay, A., & Huang, J. (2024). Deep Learning Aided Robust RSRP Prediction in Cellular Networks. 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 1–5. https://doi.org/10.1109/vtc2024-fall63153.2024.10757590
Abstract:
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.
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Funding Info:
This research / project is supported by the National Research Foundation (NRF) Singapore - Industry Alignment Fund (Pre-Positioning) for Urban Solutions and Sustainability Domain, Research Innovation Enterprise 2020 Plan (RIE2020)
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This research / project is supported by the National Research Foundation (NRF) Singapore - Energy Grid 2.0 Programme (Future Proof Reliable and Resilient Wireless Communications for Virtual Power Plant (W-VPP))
Grant Reference no. :