Reducing Energy Consumed by Repositioning of Flying Base Stations Serving Mobile Users

Bečvář, Zdeněk; Mach, Pavel; Nikooroo, Mohammadsaleh

doi:10.1109/wcnc45663.2020.9120682

Cited by 7 publications

(10 citation statements)

References 24 publications

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“…The literature on UAV-assisted communication systems is thoroughly evaluated in this subsection. In recent years, numerous number of studies were done in the field of UAV-assisted wireless networking [16][17][18][19][20][21][22][23][24][25], but only a few studies have looked at the flying regulations [26,27]. In [8], the authors presented a survey on the most recent research possibilities and problems in the field of UAV aided wireless networks.…”

Section: Related Workmentioning

confidence: 99%

“…The agent follows -greedy [52] policy to take random actions initially-which is referred to as exploring-and decays through iterations-which is referred to as exploiting-for decreasing random actions. Given that the main goal of this study is to optimize energy consumption of the UAV along with maximizing user coverage, the reward function in the proposed method is inline with the objective function in (23), and depends on the energy consumption and coverage.…”

Section: Proposed Q-learning Based Uav Positioning Mechanismmentioning

confidence: 99%

“…goes beyond the dimensions of the environment, • flies on the NFZ, • does not respect any other constraint in (23).…”

mentioning

confidence: 99%

“…On the other hand, a reward function is designed for the cases where the UAV BS is not in one of the states listed above. Since the main goal is to optimize energy consumption along with maximizing number of user covered, the reward is defined inline with the optimization objective in (23), such that…”

mentioning

confidence: 99%

“…The selection of the reward function as in (24) (i.e., making it equal to h( C)) is a legitimate decision, because the the objective of the developed Q-learning algorithm is to maximize the reward, R, and the objective function in (23) is the maximization of h( C). Thus, making the reward equal to h( C) is completely inline with the model in (23).…”

mentioning

confidence: 99%

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

et al. 2021

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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.

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