# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
## 3 Autonomous Features in UAV Networks
### Resource Management and Network Planning  
Optimal management and planning of network resources are very crucial for the success of any network. This is particularly true when it comes to networks where human intervention is minimal. For UAVs, this feature is being focused on by many researchers, and various challenges are being identified and addressed. Table 2 provides a summary of related surveys on UAV network resource management and planning.  
\begin{table}
\begin{tabular}{|p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}|} \hline
**Scope** & **Autonomous Features** & **Computational Intelligence** & **Channel Modeling** & **Interference Management** & **Security and Safety** & **Reference** \\ \hline Cooperative path planning, collisions & Path planning & \(\surd\) & Non-convex modeling & - & - & [31] \\ \hline Cooperative path planning, resource allocation & Path planning & \(\surd\) & - & - & - & [32] \\ \hline Cooperative UAVs, trajectory conflicts & Conflict detection and resolution & \(\surd\) & - & - & - & [33] \\ \hline Channel modeling & & \(\surd\) & Shadowing channel & \(\surd\) & - & [35] \\ Interference management & Path planning & \(\surd\) & Rician distribution & \(\surd\) & - & [36] \\ \hline Interference-aware path planning & Path planning & \(\surd\) & Free-space path loss with 6 GHz & \(\surd\) & - & [36] \\ \hline Interference management & Spatial configuration & \(\surd\) & Customized & \(\surd\) & - & [38] \\ Interference management & Transmission power, trajectory planning & \(\surd\) & Large-scale path loss & \(\surd\) & - & [40] \\ \hline Interference management & Transmission power & \(\surd\) & Free-space path loss & \(\surd\) & - & [41] \\ \hline Resource management & Energy consumption, transmission power & \(\surd\) & Various & \(\surd\) & - & [42] \\ \hline Collision free navigation & Trajectory planning & \(\surd\) & - & - & - & [43] \\ \hline Risk-aware path planning & Path planning & \(\surd\) & - & - & - & [44] \\ \hline \end{tabular}
\end{table}
Table 2: Summary of related survey: UAV network resource management and planning.  
Figure 3: Autonomous features classification for UAV networks.  
In [42], the autonomous distribution of resource management for UAV networks is being discussed. The authors have used the concept of game theory and highlighted various game models that may be used for optimal resource management among UAVs. In specific, five-game theory models were discussed, including coalition, potential, graphical, mean-field, and Stackelberg. Each model is explained based on its goals, design of utility function, and strategies, which implies their application areas.  
Reference [43] focused on real-time planning for UAV's path under dynamic conditions. A discrete algorithm, along with a probabilistic graph, is being used to achieve a path with no collisions. Another similar study to explore path planning for UAVs is conducted in [44]. In this study, the information from both static and dynamic paths is utilized to provide a path. A step-by-step adaptive path planning technique is proposed to achieve optimal results. Another interesting idea is that UAVs are considered additional users in the 5G cellular networks [45]. The study highlights various challenges that are encountered by service providers to facilitate new users.