# Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage
## 2 Background
derived a Reinforced Learning approach using Q learning as the base to guarantee QoS to users through optimal DC deployment. Through simulations, it was proved that given enough time to learn the environment and processing time, the DCs are able to improve the QoS of the network considering the user's movement. With this approach, the agility and the flexibility of future DA-RAN infrastructure can be kept intact [31]. A few constraints dictate the sufficient working and optimization of DA-RANs such as Drone-to-User (D2U), Drone-to-Base (D2B) links, operational constraints due to limited battery, interference from other DCs, interference from terrestrial RAN, weather conditions, etc. Most important from the above constraints is the Drone-to-Base and Drone-to-Ground (D2G) connection link. It is essential for the small DCs to be connected to an existing terrestrial RAN to function properly, since the main purpose of the DCs is to reliably relay data between the BS and connected users in DAs. [PERSON], [PERSON] et al. conducted extensive research on the D2B back-haul links regarding multiple DC deployments with a minimum number of DCs needed for user coverage by implementing a heuristic algorithm. As part of related research in [32; 33], the authors also investigated the optimal 3D back-haul placement of DCs by two different approaches, namely, user-centric and network-centric [33]. It was concluded that the network-centric approach maximizes the total number of users served regardless of the network required data rate, while the user-centric approach maximizes their sum rate. A frequently used meta-heuristic technique used to deal with network coverage and resource allocation is Particle Swarm Optimization (PSO); it was adapted by [PERSON], [PERSON] et al. in their paper and was modified to fit a particular problem, i.e., 3D placement of DBSs or DCs in a DA-RAN. [PERSON], [PERSON] et al. implemented a utility function into PSO to maintain the capacity and find the coverage constraint for each drone. The utility function that successfully satisfied the coverage constraints while maintaining the capacity constraints was marked as the placement for the DBS. The proposed solution was later simulated in a 2D and 3D environment. Through simulations, it was noted that the DBS decreases its altitude in dense areas to reduce the interference to uncovered users and increases its altitude in low-density regions to cover large areas [32]. PSO was implemented by [PERSON], [PERSON] et al. [34] for the cell planning problem for fourth-generation (4G) cellular networks. The main aim of the research was to satisfy both the cell coverage and the capacity constraints simultaneously. PSO was used to find the sub-optimal location for the BS that satisfied both the aforementioned constraints in any DAs. For the coverage and cell capacity constraints, many other factors such as the subarea category, user density, resource allocation and inter-cellular interference were also taken into account. Subsequent measures were also taken to remove the possibility of redundant BS placement and keep the inter-cellular interference and the number of BS placements to a minimum. PSO was again adapted by [PERSON], [PERSON] et al. to solve the 3D spatial deployment problem of DCs in a DA-RAN. The proposed solution took inspiration from the D2U and D2B path loss models explained in [27] and proposed a model where PSO was implemented in every DC independently, and the algorithm was further optimized to cancel a portion of the search space when an optimal position for the DC was found. The resulting algorithm was coined as per Drone Iterated Particle Swarm Optimization (DI-PSO). This results in the algorithm reaching a global optimum with much higher probability, and since the search space for the DC is updated after each iteration with only uncovered DAs remaining, the inter-droneinterference is also kept to a minimum. Simulation testing and results also show that there is a much higher convergence rate in DI-PSO when compared with standard PSO [35].