Dynamic deployment of UAVs for temporary networks using multi-criteria decision-making

Unmanned Aerial Vehicle Base Stations (UAV-BSs) are effective to support mobile wireless systems in situations where an unusually high density of users require enhanced coverage and capacity such as in large sport events or music festivals. However, the joint deployment problem of UAV-BSs is NP-hard, and the optimization methods used to solve such a class of problems are often too slow for (quasi-)real-time applications when the density of users and the number of UAV-BS are high. This inefficiency creates a need for more adaptable methods to solve the UAV-BS positioning problem. This paper proposes a solution by transforming the UAV-BSs’ placement problem into a decision-making process using the Analytic Hierarchy Process (AHP) method. Our solution considers that all UAV-BSs move along scanning points with a predetermined path, each with a minimal distance from one another. The proposed algorithm, named UAV-AHP, efficiently determines the UAV-BSs’ positions, thereby improving the network performance based on (quasi-) real-time acquisition of user signals at scanning points. For the high-density scenarios under investigation, our numerical results demonstrate that UAV-AHP outperforms commonly used heuristics that sub-optimally solve NP-hard problems, namely, cuckoo search (CS), particle swarm optimization (PSO), and a genetic algorithm (NSGA-II). The proposed method for UAV-BS deployment requires considerably lower running times to find satisfactory solutions than CS, PSO, and NSGA-II. • Proposed algorithm called UAV-AHP jointly determines UAV base stations’ positions to improve the network performance based on user data collected at specific scanning points. • Flexible implementation that enables the selection of criteria prioritization for different network performance indicators. • Satisfactory results for high density scenarios in few seconds only, outperforming other heuristics in terms of reduction in computational cost.