Deep Learning Assisted Fixed Wireless Access Network Coverage Planning

Abstract

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

Keywords

• Fixed wireless access network
• coverage planning
• convolutional neural network
• instance segmentation