TY - JOUR
T1 - Energy Harvesting in Unmanned Aerial Vehicle Networks with 3D Antenna Radiation Patterns
AU - Turgut, Esma
AU - Cenk Gursoy, M.
AU - Guvenc, Ismail
N1 - Funding Information:
Manuscript received September 17, 2019; revised March 24, 2020 and June 19, 2020; accepted June 22, 2020. Date of publication July 7, 2020; date of current version November 20, 2020. This work was supported by the National Science Foundation under Grant CCF-1618615. This article was presented in part at the IEEE Vehicular Technology Conference (VTC)-Fall, Honolulu, HI, USA, Sep. 2019. The associate editor coordinating the review of this article and approving it for publication was K. Huang. (Corresponding author: M. Cenk Gursoy.) Esma Turgut and M. Cenk Gursoy are with the Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244 USA (e-mail: eturgut@syr.edu; mcgursoy@syr.edu).
Publisher Copyright:
© 2017 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - In this paper, an analytical framework is provided to analyze the energy coverage performance of unmanned aerial vehicle (UAV) energy harvesting networks with clustered user equipments (UEs). Locations of UEs are modeled as a Poisson Cluster Process (PCP), and UAVs are assumed to be located at a certain height above the center of user clusters. Hence, user-centric UAV deployments are addressed. Two different models are considered for the line-of-sight (LOS) probability function to compare their effects on the network performance. Moreover, antennas with doughnut-shaped radiation patterns are employed at both UAVs and UEs, and the impact of practical 3D antenna radiation patterns on the network performance is also investigated. Initially, the path loss of each tier is statistically described by deriving the complementary cumulative distribution function and probability density function. Following this, association probabilities with each tier are determined, and energy coverage probability of the UAV network is characterized in terms of key system and network parameters for UAV deployments both at a single height level and more generally at multiple heights. Through numerical results, we have shown that cluster size and UAV height play crucial roles on the energy coverage performance. Furthermore, energy coverage probability is significantly affected by the antenna orientation and number of UAVs in the network.
AB - In this paper, an analytical framework is provided to analyze the energy coverage performance of unmanned aerial vehicle (UAV) energy harvesting networks with clustered user equipments (UEs). Locations of UEs are modeled as a Poisson Cluster Process (PCP), and UAVs are assumed to be located at a certain height above the center of user clusters. Hence, user-centric UAV deployments are addressed. Two different models are considered for the line-of-sight (LOS) probability function to compare their effects on the network performance. Moreover, antennas with doughnut-shaped radiation patterns are employed at both UAVs and UEs, and the impact of practical 3D antenna radiation patterns on the network performance is also investigated. Initially, the path loss of each tier is statistically described by deriving the complementary cumulative distribution function and probability density function. Following this, association probabilities with each tier are determined, and energy coverage probability of the UAV network is characterized in terms of key system and network parameters for UAV deployments both at a single height level and more generally at multiple heights. Through numerical results, we have shown that cluster size and UAV height play crucial roles on the energy coverage performance. Furthermore, energy coverage probability is significantly affected by the antenna orientation and number of UAVs in the network.
KW - 3D antenna radiation patterns
KW - Poisson cluster processes
KW - Poisson point processes
KW - Thomas cluster processes
KW - Unmanned aerial vehicles (UAVs)
KW - energy coverage probability
KW - energy harvesting
KW - stochastic geometry
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U2 - 10.1109/TGCN.2020.3007588
DO - 10.1109/TGCN.2020.3007588
M3 - Article
AN - SCOPUS:85088796640
SN - 2473-2400
VL - 4
SP - 1149
EP - 1164
JO - IEEE Transactions on Green Communications and Networking
JF - IEEE Transactions on Green Communications and Networking
IS - 4
M1 - 9134425
ER -