# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 2 Related Works  
Generally, two types of algorithms have been used for solving global path planning problems: classical and intelligent algorithms. The former includes the Dijkstra algorithm [26], the A-star algorithm [27], simulated annealing [28], and differential evolution [29]. However, the main shortcomings of such algorithms are their need for large memory and extended run time. Meanwhile, intelligent optimization algorithms, such as evolutionary and swarm intelligence algorithms, have become more popular for solving path planning problems due to their inherent features and design principles, such as flexibility, robustness, high search capability, and accuracy. Over the past decade, different intelligent optimization algorithms have been utilized to solve path planning problems in robotics, such as ant colony algorithm [30], firefly algorithm [31], artificial bee colony [32], GA [33], PSO [34], and whale optimization algorithm [35]. In addition, heuristics intelligent optimization algorithms have also been widely used to solve local path planning optimization problems, such as graph-based algorithms [36], heuristic search algorithms [37], field-based algorithms [38], and intelligent optimization algorithms [39]. Most of the algorithms utilized to solve UAV path planning problems have originally adapted the existing algorithms for robot path planning. Articles such as [40; 41] thoroughly surveyed the most commonly used path planning algorithms. The authors in [42] proposed an improved crossover operation for solving 2D path planning problems for autonomous mobile robots using GA in a static environment. In [43], the wolf pack algorithm was utilized to solve the multiobjective 3D path planning problem for drones. The authors improved the algorithm performance by applying the mutation and crossover operations of GA to the wolf pack algorithm. Ref. [44] proposes a UAV path planning algorithm based on the random tree planning technique while accommodating real-time traffic and geofencing constraints. The proposed algorithm can terminate tree expansion based on the defined criteria, resulting in lower computational complexity and making the algorithm suitable for onboard execution. The authors in [45] formulated a UAV trajectory problem to minimize the flight time while ensuring the outage performance during the mission. To reduce the problem complexity, a low-complexity method was proposed to find the shortest path in an undirected weighted graph with enlarged cellular coverage. Ref. [46] considers the effect of 3D antenna radiation patterns and backhaul constraints on the path planning of UAV-assisted wireless networks. It was considered that the UAV acts as a relay and the object to optimize the trajectory for improving the wireless coverage for terrestrial uses. The authors in [47] utilized the PSO algorithm to maximize the throughput of a UAV-assisted wireless network by adjusting the flight height and transmission power of the UAV. The simulation results showed that the PSO can rapidly converge to an optimal solution for the considered problem. In [5], an enhanced version of PSO (EPSO) was utilized for UAV path planning in a large-scale remote environment. The results showed the effectiveness of EPSO in solving the UAV path planning for aerial-based data collection in a wireless sensor network. Table 1 presents a summary of the reviewed studies with a description of the application, optimization approach, key findings, and limitations.