Enhanced Deployment Strategy for the 5G Drone-BS Using Artificial Intelligence

Abstract

The use of drones to perform various task has recently gained a lot of attention. Drones have been used by traders to deliver goods to customers, scientists, and researchers to observe and search for endangered species, and by the military during critical operations. The flexibility of drones in remote controlling makes them ideal candidates to perform critical tasks with minimum time and cost. In this paper, we use drones to setup base stations that provide 5G cellular coverage over a given area in danger. The aim of this paper is to determine the optimum number of drones and their optimum location, such that each point in the selected area is covered with the least cost while considering communication relevant parameters such as data rate, latency, and throughput. The problem is mathematically modeled by forming linear optimization equations. For fast optimized solutions, genetic algorithm (GA) and simulated annealing (SA) algorithms are provisionally employed to solve the problem, and the results are accordingly compared. Using these two meta-heuristic methods, quick and relatively inexpensive feedback can be provided to designers and service providers in 5G next generation networks.

Keywords

• Genetic algorithm
• simulated annealing
• UAV
• smart city
• IoT