Cell-Level RSRP Estimation With the Image-to-Image Wireless Propagation Model Based on Measured Data

Abstract

Wireless propagation models play a significant role in the deployments of base stations that are used to the reference signal receiving power (RSRP) of signal receivers in a cell. However, the existing models predict the RSRP of one receiver point in a cell at a time, which cannot be generalized to other cells. Motivated by this, a cell-level RSRP estimation method is proposed to directly predict the whole-cell RSRP by converting the RSRP estimation into an image-to-image translation. First, an environment map of each cell and measured RSRP for each cell is transformed into an image. Second, a cell-level image-to-image wireless propagation model based on conditional generative adversarial networks is proposed, which can directly predict the whole-cell RSRP at a time. In particular, a residual estimation method is proposed for the measurement RSRP data in the real world. The proposed method employs an empirical model to reveal the wireless propagation law as a priori knowledge and guide the training steps of the deep learning model. Finally, the experimental results verify the accuracy and generalization performance of the proposed image-to-image wireless propagation model.