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

Abstract

A communication system based on unmanned aerial vehicles (UAVs) is a viable alternative for meeting the coverage and capacity needs of future wireless networks. However, because of the limitations of UAV-enabled communications in terms of coverage, energy consumption, and flying laws, the number of studies focused on the sustainability element of UAV-assisted networking in the literature was limited thus far. We present a solution to this problem in this study; specifically, we design a Q-learning-based UAV placement strategy for long-term wireless connectivity while taking into account major constraints such as altitude regulations, nonflight zones, and transmit power. The goal is to determine the best location for the UAV base station (BS) while reducing energy consumption and increasing the number of users covered. Furthermore, a weighting method is devised, allowing energy usage and the number of users served to be prioritized based on network/battery circumstances. The suggested Q-learning-based solution is contrasted to the standard k-means clustering method, in which the UAV BS is positioned at the centroid location with the shortest cumulative distance between it 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.

Energy-Efficient UAV Movement Control for Fair Communication Coverage: A Deep Reinforcement Learning Approach

“…The capacity, coverage, and energy efficiency problems were studied in [40,41]. In [40], Atli et al developed a Q-learning-based UAV placement strategy to solve the coverage and capacity needs in terms of transmit power, altitude regulations, and non-flight zones for long-term wireless communication. They focused on finding the best location for the UAV-BS that would reduce the energy consumption and increase the coverage score.…”

“…The fully charged energy of the battery was E max = 1×10 5 joules. The channel characteristics of the UAV network followed the urban environment with path-loss exponent (n 0 = 2.5), and more settings of the network are summarized in Table 3 based on [31,36,40,44,45].…”

Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment

“…Equipped with advanced navigation systems and smart sensors, UAVs are currently employed in a range of applications, including surveillance, search-and-rescue missions, and on-demand communication services. As UAV technology matures and regulations evolve, the global UAV market is poised for substantial growth [2][3][4].…”