Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

# Multi-objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones

Version 1 : Received: 1 September 2021 / Approved: 9 September 2021 / Online: 9 September 2021 (11:28:38 CEST)

How to cite: Atlı, İ.; Ozturk, M.; Valastro, G.C.; Asghar, M.Z. Multi-objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones. Preprints 2021, 2021090177 (doi: 10.20944/preprints202109.0177.v1). Atlı, İ.; Ozturk, M.; Valastro, G.C.; Asghar, M.Z. Multi-objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones. Preprints 2021, 2021090177 (doi: 10.20944/preprints202109.0177.v1).

## Abstract

Unmanned aerial vehicles (UAVs)-based communication system is a promising solution to meet coverage and capacity requirements of future wireless networks. However, UAV-enabled communications is constrained with its coverage, energy consumption, and flying regulations, and the number of works focusing on the sustainability aspect of UAV-assisted networking has been limited in the literature so far. In this paper, we propose a solution to this limitation; particularly, we design a $Q$-learning-based UAV positioning scheme for sustainable wireless connectivity considering key constraints, that are, altitude regulations, non-flight zones, and transmit power. The objective is to find the optimal position of the UAV base station (BS) and minimize the energy consumption while maximizing the number of users covered. Moreover, a weighting mechanism is developed, where the energy consumption and number of users covered can be prioritized according to network/battery conditions. The proposed Q-learning-based solution is compared to the baseline k-means clustering method, where the UAV BS is positioned at the centroid location that minimizes the cumulative distance between the UAV BS and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.

## Keywords

Sustainable wireless connectivity; Energy saving; UAV; Communication system; 5G; Positioning; Reinforcement learning

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