# Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage
## 2 Background
Other related work in terms of network coverage, resource allocation and capacity include [34] include studies where instead of relying on drone cells, the algorithm finds the optimal place for setting up new Base Stations, implementing both PSO and Grey Wolf Optimization (GWO) [36] and studying their performances. The proposed method for an optimal Base Station location first includes placing the Base Stations in a random location owning to the random behavior of PSO and GWO and then eliminating the eventually redundant Base Stations. The redundant Base Stations are removed based on the assumption that if the absence of a Base Station affects at least one of the optimization problem constraints, then the Base Station has to be kept and assumed indispensable for a safe network operation [34]. A Balanced Grey Wolf Optimization (B-GWO) has been proposed for multi-UAV deployment and power allocation in Non-Orthogonal Multiple Access (NOMA); although different from DA-RAN, this implementation can be used for comparison or to gain insight into DC deployment and allocation [37]. Another researcher proposed the usage of GWO in Path Planning, where a Hybrid Discrete Grey Wolf Optimizer (HDGWO) has been used to optimize a multi-UAV path planning model with energy constraints (MUPPEC). GWO was also used by [PERSON], [PERSON] et al. as one of many swarm intelligence algorithms for DC placement, and then later, all the implemented algorithms were compared. Among the implemented algorithms, GWO was found to be the most effective in DC placement according to [PERSON] and [PERSON] tests [38]. Similar to user coverage using DCs, differential evolution (DE) has been employed to form a UAV-Assisted IoT Data Collection System with a variable population named differential evolution with a variable population size (DEVIPS) [39]. Similar to user coverage, the DCs in DEVIPS are deployed to have the best possible deployment position for the coverage of IoT devices. DE has also been implemented for a single unmanned aerial vehicle multitasking, since as an optimized problem, the efficiency of standard DE to obtain the global optimal solution is very low; therefore, an improved adaptive differential evolution algorithm has been implemented for this purpose [40]. The proposed algorithm can then adopt the mutation factor and crossover factor into a dynamic adaptive function which makes the above-mentioned factors able to adjust with the number of population iterations and individual fitness values. Much more recent developments in on-demand connectivity enablers have been conducted in [41], where Flying Base Stations or FBSs are deployed on the demanded network and based on user-specific requirements. Two different meta-heuristic models, Cuckoo search and differential evolution, were implemented with feasible results being provided by a novel repair operator and compared in various scenarios under constraints such as uplink and downlink rates, capacity of the FBS, etc. Extensive testing has shown that both models perform relatively well in covering all associated users in the demanding area, with the only major difference being that DE is three to six times faster in computation than the Cuckoo search algorithm.