DeepREM: Deep-Learning-Based Radio Environment Map Estimation From Sparse Measurements

Abstract

Radio environment maps depict the coverage area of cellular networks. They are usually estimated by interpolating sparse measurements gathered in test drives. Typical estimation techniques rely on physical or statistical propagation models, known base station locations, topographic data, and/or building data. In this paper, we present DeepREM: a set of two deep-learning models (U-Net and CGAN) that estimate REMs from sparse measurements without requiring any additional information. A physical ray-tracing simulator with geographic and building data is required during the model training, but not for its operation afterwards. DeepREM models are capable of estimating two radio parameters: ${i}$ ) reference signal received power (RSRP) and ii) BS coverage (cell indices). Extensive testing shows that DeepREM models outperform state-of-the-art methods in terms of root mean squared error (RMSE) and mean absolute error (MAE), and that CGAN has better generalization capabilities in the analyzed scenarios (in particular when the input distribution does not fit the training dataset). Achieved RMSE and MAE are 6.32 and 4.54 dBm for RSRP estimation, while error rate was around 11% for BS coverage estimation. Moreover, our training dataset and models are publicly available and can be used to speed up and improve the accuracy of current REM estimation techniques.

Keywords

• Deep learning
• radio environment maps
• U-Net
• conditional GAN
• image-to-image translation
• image denoising