Reference Signal Received Power Prediction Using Convolutional Neural Network with Residual Loss - Thearrawit Ngenjaroendee; Watid Phakphisut; Thongchai Wijitpornchai; Poonlarp Areeprayoonkij; Tanun Jaruvitayakovit; Nattakan Puttarak (My University, 2023)
In this paper, LTE measurement reports collected from user equipments are used to generate the residual loss, which can represent the loss value of each grid. The residual loss and geospatial data are used in the learning process of convolutional neural network (CNN). We also use the site configuration and three-dimensional antenna pattern. Thus, the neural network and convolutional neural network are proposed to construct deep learning to predict the reference signal received power (RSRP) in Bangkok, Thailand. The results show that residual loss can improve the efficiency of prediction.
Keywords: Millimeter-Wave Propagation and Modeling; Advanced MIMO Systems Optimization; Wireless Signal Modulation Classification