# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 2 Related Works
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref.** & **Application** & **Environment/Scenario** & **Objectives)** & **Approach** & **Key Findings** & **Limitations** \\ \hline
[32] & Combat UAV path planning & Flat combat field with threats and other constraints & Minimize traveling path & ABC algorithm improved by a balancing-evolution strategy & Improved performance compared with ABC by enhancing the local and global global regulation & Limited to 2D path planning and few constraints \\ \hline
[33] & UAV path planning during emergency landing & Different scenario with different levels of difficulty & Minimize changes and enhance & Greedy heuristic, genetic algorithm (GA), and multiple optimization & Genetic algorithms return better quality solutions within a reasonable computational time & The focus of the problem was on the landing procedure, not the entire path \\ \hline
[34] & 3D UAV path planning & Digital map with face mountains or threats & Minimize the traveling path, under fuel, treat, and altitude cost functions & Improved chaos particle swarm optimization (PSO) & Overcome the landscape of the PSO algorithm, which falls into local optimum and slow & Restricted to only considering mountains \\ \hline
[36] & 3D UAV routing with collision avoidance & 3D operating spaces & Collision avoidance & Graph theory-based algorithm & Reduced algorithm complexity and developed a novel, adaptive, graph search scheme & Parameters such as computational time and energy efficiency were not considered \\ \hline
[42] & General 2D UAV path planning & Static environment & Minimize energy consumption & GA with improved crossover operation & Improved convergence speed and improved CA performance & 2D path planning is limited to the few constraints for terrestrial robots \\ \hline
[43] & 3D quasi-optimal UAV path & Path planning for rotor using UAVs in the complex planning & Path planning for rotor using UAVs in the complex flight, and path smoothness & Minimize multiple cost functions & Improved the performance of algorithm & Slow convergence rate, which can improve by applying crossover and the path to the exploration rate, is also limited to any constraints \\ \hline
[44] & UAV BVLO’s path planning & Erbing while accommodating real-time & Offside detection and avoidance & Rapidly exploring random tree planning & Sudden for limited onboard scheduling & Specifically designed designed designed for obstacle avoidance and computationally expensive large-scale energy consumption & Asplience and complexity for large-scale energy consumption & Asplience and complexity for large-scale energy consumption \\ \hline
[45] & Cellular-enabled UAV & Single UAV and multiple ground control stations & Minimize flight time, while consecutively outage & Minimizing flight time, while consecutively outage & Greedy algorithm based on graph theory & Proposed a low-complexity method to obtain an approximate optimal solution & Proposed a low-complexity method to obtain an approximate optimal solution & Improved a low-complexity method to obtain an approximate optimal solution & Ingrid height constraints and obstacles were not considered \\ \hline
[46] & UAV-assisted cellular network & UAV works as a relay to provide downlink coverage & Maximizing total sum rate and minimizing flight time & Deep learning based & Improved the signal quality in the backhaul list of cellular network & Constraints such as obstacles and power consumption have not been considered \\ \hline
[47] & UAV-assisted cellular network & UAV works as a relay to provide downlink coverage & Maximize system throughput by optimizing altitude and transmission power & PSO & Outgregrement conventional fixed altitude and host transmit power approaches & Issues such as energy efficiency and the presence of obstacles have not been considered \\ \hline \end{tabular}
\end{table} 
Table 1: A summary of the reviewed UAV path planning models.