Deep Learning Assisted Fixed Wireless Access Network Coverage Planning

Wireless network coverage planning is crucial for mobile operators and fixed wireless providers to estimate the performance of their networks plan future antenna mast deployments. To generate accurate maps target buildings, traditional tools either require manual input Customer-Premises Equipment (CPE) locations or need compute received signal strength from nearby Access Points (APs) all geolocations in area interest which consumes computational resource unnecessarily. In this paper we propose a Deep Learning (DL) based universal enhancement automatically extracts potential CPE aerial images buildings. We evaluate pixel level object detection provided by Mask Region-based Convolutional Neural Network (Mask R-CNN) trained on an image dataset with suburban rural residential properties across North Yorkshire, UK. also demonstrate complete task flow informative building reports while combining DL WISDM industrial system.