Intelligent network planning tool for location optimization of unmanned aerial vehicle base stations using geographical images

Nowadays, automated and intelligent solutions are required by different services and applications to satisfy current industrial necessities. Internet of things/robotic devices have been found useful in the collection and processing of data for pattern recognition, analyzing and anticipating incident information, optimization, and provision of better and timely decision-making to enable network providers to measure the quality of services, thereby ensuring efficiency. Telecommunication systems need reliable and fast internet networks for connected devices and other business activities; thus, technologies are being explored to determine efficient ways of planning and installing base stations. Recently, possibilities of deploying unmanned aerial vehicles (UAVs) as base stations had been investigated. Although successes have been recorded, not much work has been done on network planning tools for location optimization of UAV base stations (UAV-BS) in 5G and beyond networks to the best of our knowledge. In this paper, an intelligent network planning tool (iNPT) for location optimization of UAV-BS has been developed with different channel models and network planning parameters. The developed tool has resources to suggest a better positioning of the UAV-BS using the simulated annealing meta-heuristic to optimize their locations. The tool also includes a battery management interface that can calculate the battery dissipation with reference to the received and transmitted power of the UAV-BS and display the estimated time of flight for the UAV-BS. Geographical information, such as the height of a specific region, obtained from a map is considered in the simulations. The results obtained using the COST231-Hata, Hata path loss, log distance, and two-ray ground reflection models have been presented in this paper. Based on the experimental results, the developed simulator can be useful in network planning tasks to improve coverage areas using UAV-BSs. In addition, the proposed tool has a friendly interface that provides geographical images, and the BSs and UAVs optimized locations. (c) 2022 SPIE and IS&T