UAV Positioning and Optimization Framework with Obstacle-Aware Modeling Ussing GEKKO

UAV Positioning System Description This software implements an algorithm for optimal UAV placement in wireless communications scenarios. The system takes into account: Building obstacles and their heights Multiple User Equipment (UE) locations and their Signal-to-Noise Ratio (SNR) requirements Line-of-sight considerations using angle calculations Power optimization for the UAV transmitter The main features include: Automatic calculation of minimum required transmission power 3D positioning that ensures line-of-sight with all UEs Building obstacle avoidance SNR requirement satisfaction for all connected UEs User Manual File Structure obstacle - Contains building corner coordinates in format: x, y, height Each line represents one corner of the building Coordinates must be listed in order to form a valid polygon UEs - Contains user equipment data in format: ID, x, y, z, SNR ID: Unique identifier for each UE x, y, z: 3D coordinates of the UE SNR: Required Signal-to-Noise Ratio in dB Usage Instructions Setup Requirements pip install numpy shapely gekko Input File Preparation Create an obstacle file with building coordinates Create a UEs file with user equipment data Place both files in a directory named UAVP Running the System python app.py Output The system will output a tuple containing: Optimal transmission power (PT) in dBm Optimal UAV position [x, y, z] Technical Notes The system uses the GEKKO optimizer for solving the positioning problem Line-of-sight is guaranteed through angle calculations The algorithm iteratively increases transmission power until a valid solution is found The UAV will always be positioned above the maximum building height Constraints All UEs must have clear line-of-sight to the UAV The UAV must be positioned above the highest building Signal strength must meet SNR requirements for all UEs Citation Requirements As per the MIT License with Citation Requirement, when using this software, please cite: Author: Kamran Shafafi DOI: 10.5281/zenodo.14566274