TY - JOUR
T1 - Cost- and Energy-Efficient Aerial Communication Networks With Interleaved Hovering and Flying
AU - Babu, Nithin
AU - Virgili, Marco
AU - Papadias, Constantinos
AU - Popovski, Petar
AU - Forsyth, Andrew
N1 - Funding Information:
Manuscript received November 21, 2020; revised April 8, 2021 and June 13, 2021; accepted July 16, 2021. Date of publication July 27, 2021; date of current version September 17, 2021. This work was supported by the project PAINLESS which has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant 812991. The review of this article was coordinated by Dr. Jung-Chieh Chen. (Corresponding author: Nithin Babu.) Nithin Babu and Constantinos B. Papadias are with the Research, Technology and Innovation Network (RTIN), Alba, The American College of Greece, Athens, Greece, and also with the Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark (e-mail: [email protected]; [email protected]).
Funding Information:
Thisworkwas supported by the project PAINLESS which has received funding from the European Union's Horizon 2020 research and innovation programme under Grant 812991.
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2021/7/27
Y1 - 2021/7/27
N2 - This work proposes a methodology for the energy-and cost-efficient 3-D deployment of an unmanned aerial vehicle (UAV)-based aerial access point (AAP), that exchanges a given amount of independent data with a set of ground user equipment (UE). Considering a fly-hover-communicate transmission scheme, the most energy-efficient 3-D hovering points (HPs) of the AAP are determined by decoupling the problem in the horizontal and vertical dimensions. First, we derive analytically the optimal hovering altitude that jointly maximizes the downlink and uplink global energy efficiency (GEE) of the system. Next, we propose the multilevel circle packing (MCP) algorithm to determine the minimal number of HPs and their associated horizontal coordinates, such that the AAP covers all the UEs in the given geographical area. A cost analysis is carried out to observe the variation of both fixed and variable costs; these are then minimized by suitably selecting the AAP's battery parameters, like the depth of discharge (DOD), defined as the portion of battery capacity that is consumed during a discharge cycle, and the velocity of the UAV. Simulation results show that: the UAV energy consumption has a significant impact on the 3-D HPs of the AAP; the time spent during the substitution swap of an out of power AAP has a major influence on the operational cost; the cost of the system can be optimized by suitably selecting the onboard battery and the UAV flight parameters.
AB - This work proposes a methodology for the energy-and cost-efficient 3-D deployment of an unmanned aerial vehicle (UAV)-based aerial access point (AAP), that exchanges a given amount of independent data with a set of ground user equipment (UE). Considering a fly-hover-communicate transmission scheme, the most energy-efficient 3-D hovering points (HPs) of the AAP are determined by decoupling the problem in the horizontal and vertical dimensions. First, we derive analytically the optimal hovering altitude that jointly maximizes the downlink and uplink global energy efficiency (GEE) of the system. Next, we propose the multilevel circle packing (MCP) algorithm to determine the minimal number of HPs and their associated horizontal coordinates, such that the AAP covers all the UEs in the given geographical area. A cost analysis is carried out to observe the variation of both fixed and variable costs; these are then minimized by suitably selecting the AAP's battery parameters, like the depth of discharge (DOD), defined as the portion of battery capacity that is consumed during a discharge cycle, and the velocity of the UAV. Simulation results show that: the UAV energy consumption has a significant impact on the 3-D HPs of the AAP; the time spent during the substitution swap of an out of power AAP has a major influence on the operational cost; the cost of the system can be optimized by suitably selecting the onboard battery and the UAV flight parameters.
KW - 3-D placement optimization
KW - Cost-optimization
KW - UAV communication
KW - energy-efficiency
U2 - 10.1109/TVT.2021.3100255
DO - 10.1109/TVT.2021.3100255
M3 - Article
SN - 0018-9545
VL - 70
SP - 9077
EP - 9087
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 9
ER -
