Autonomous Bio-inspired 3D Deployment of Aerial Base Stations for Optimal Wireless Coverage in 5G Networks

Unmanned aerial vehicles (UAVs) have recently emerged as enablers for a range of use cases in 5G networks, one of which uses them as airborne base stations to intermittently service mobile customers in emergency scenarios, uncovered or hard-to-reach places. The employment of a completely bio-inspired model in the navigation and placement of these UAVs is a challenging task. In this paper, we propose a bio-inspired neuron model for addressing the problem of deploying multiple UAV-mounted base stations optimally in 3D space while adopting their positions as users move around within the 5G network. We formulate the 3D deployment problem as a vector model using electrostatic forces and neurovectors, a representation of vectors in space using populations of neurons. The model was tested and evaluated based on various parameters, according to different scenarios. The results have demonstrated that our neurovector-based solution offers efficient performance in terms of data rate and high agility to navigate around obstacles and complex environments. Moreover, our solution outperforms existing approaches in their adaptability to users’ movements. As it maintains superior stability with reduced fluctuations, and ensures a high percentage (around 90%) of connected users. In addition, the proposed solution optimizes UAV speed to minimize energy consumption, contributing to enhanced coverage duration and overall sustainability.