Location-Aware Deep Neural Network for Predicting Indoor 5G RSSI and CQI Using Drone-Based External RF Sensing

Achieving reliable indoor 5G coverage in high-rise urban environments is challenging due to the significant signal attenuation of higher frequency bands through building materials and the intricate layouts of modern architecture. Traditionally, assessing in-building coverage to meet mobile network operators’ standards requires conducting indoor walk tests, a method that is both costly and time-consuming. This study aims to develop a scalable and cost-effective method for predicting and visualizing in-building 5G coverage using externally collected radio frequency (RF) data. We propose a methodology that employs AIoT-enabled drones equipped with cellular network scanners to gather key signal metrics, such as Received Signal Strength Indicator (RSSI) and Channel Quality Indicator (CQI), from the dominant 5G macrocell outside the building. The proposed Location-Aware Deep Neural Network (LA-DNN) integrates external radio frequency (RF) measurements with spatial coordinates, free-space path loss models, and material-specific attenuation factors to predict indoor RSSI and CQI. The model demonstrates robust predictive performance, achieving an R 2 of 0.9567 with a root mean squared error (RMSE) of 8.75 dBm for RSSI, and an R 2 of 0.9713 with an RMSE of 2.08 for CQI, validated against indoor walk-test data. Additionally, three-dimensional (3D) heatmaps are generated to visualize predicted coverage across building floors, facilitating the identification of coverage gaps and RF null zones. By minimizing the need for indoor measurements during initial in-building coverage assessments, the proposed framework significantly enhances the efficiency of site survey teams before implementing In-Building Coverage (IBC) systems.