Proactive Coverage Area Decisions Based on Data Field for Drone Base Station Deployment
Abstract
:1. Introduction
 The coverage area of DBSs: One of the greatest challenges is to identify the proactive coverage area (PCA) that a DBS needs to cover. Especially in an overload condition caused by burst crowd traffic, the PCAs enclosed with more UE covered by the DBS benefits the network the most. Moreover, when the PCAs are determined, how to allocate DBSs to cover these areas is also one of the problems to be solved.
 The number of DBSs and the total energy consumption need to be considered. The authors assume that each DBS has a minimum and a maximum vertical altitude. Moreover, the energy consumption of a DBS is related to its altitude. Indeed, the higher the altitude, the larger the covered area, the higher the energy consumption. Thus, the ondemand coverage radius and the optimal altitude, as two key cost metrics, should be considered.
 A novel method is proposed for deciding the PCAs. According to data field theory, a demand point with a larger potential value has more demand points gathered around it [8]. Then, the region centering on the demand point with local maximum potential value can be decided as a PCA. Compared to a heuristic DBS location decision, assigning DBSs to cover the decided PCAs has a lower complexity.
 To cover the decided PCAs by the supplied DBSs, treated as which DBSs serve which PCAs problem, the authors design the “firstbesteffort and secondpatching” (FBE–SP) algorithm to solve the problem.
 Meanwhile, the minimum energy cost mechanism is employed in this paper. The authors further consider the ondemand coverage radius and the optimal altitude as two cost metrics. The ondemand coverage radius is determined by the size of the area that the DBS actually needs to cover, while the corresponding optimal altitude can be more simply obtained by solving the linear equation between altitude and coverage radius.
2. Related Works
3. System Model and Problem Formulation
3.1. System Model
3.2. Problem Formulation
4. Efficient Solution for the MCMC Problem
4.1. Deciding Proactive Coverage Areas
Algorithm 1. PCAs decision algorithm for DBSs placement 

4.2. PlacingAndOperating the DBSs
Algorithm 2. FirstBestEffort and SecondPatching algorithm (FBE–SP) 
Initialization: Set the number of supplied DBSs as K, and the corresponding capacity is U_{DBS} of each DBS, a set of congested UE of PCA, A = {a_{1}, a_{2}, …, a_{L}}, where a_{j} represents the numbers of UE in the $j$th PCA, . denotes the cardinal number. 

4.3. 3D Location Optimizing for Energy Efficiency
4.4. Complexity Analysis
5. Simulation Analysis
5.1. Simulation Implementation
5.2. Performance Evaluation
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
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$({\mu}_{LoS},{\mu}_{NLoS})$  (1,20)  (0.1,21) 
$(a,\beta )$  (9.61,0.16)  (4.88,0.43) 
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Hu, B.; Wang, C.; Chen, S.; Wang, L.; Yang, H. Proactive Coverage Area Decisions Based on Data Field for Drone Base Station Deployment. Sensors 2018, 18, 3917. https://doi.org/10.3390/s18113917
Hu B, Wang C, Chen S, Wang L, Yang H. Proactive Coverage Area Decisions Based on Data Field for Drone Base Station Deployment. Sensors. 2018; 18(11):3917. https://doi.org/10.3390/s18113917
Chicago/Turabian StyleHu, Bo, Chuan’an Wang, Shanzhi Chen, Lei Wang, and Hanzhang Yang. 2018. "Proactive Coverage Area Decisions Based on Data Field for Drone Base Station Deployment" Sensors 18, no. 11: 3917. https://doi.org/10.3390/s18113917