## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches
#### 8.1.4 Flight and Transmission Scheduling  
Here, we consider studies that involve the scheduling UAV-BSs flights as well as their sequence of transmission in order to ensure that minimum energy is consumed in the process. We also examine research works that investigate how the fight of UAVs are coordinated and scheduled or how UAVs cooperate to achieve energy-efficient communication. Conventional Approaches  
A flight control framework for multi-UAV systems in order to minimize the energy consumption of the UAV was proposed in [256]. The velocity control of each UAV was first modeled using a MFG theory, after which a Cucker-Smale (CS) flocking algorithm was developed to control the velocity in order to achieve minimum energy consumption in the network. An MDP-based framework for jointly scheduling data transmission and task computations in order to maximize the EE of a UAV-based network was proposed in [257] while considering a stochastic trajectory. A probabilistic scheduling algorithm based on linear programming was proposed to solve the joint transmission and computation scheduling problem in order to minimize the total energy consumption of the network. The authors in [29] studied the formation of swarms in multiple UAV networks to minimize the energy consumption of the UAVs. A joint optimization problem comprising transmit power, beamforming vectors, and swarm formation was formulated, after which a heuristic algorithm was developed to determine the optimal swarm formation that would result in minimum energy consumption in the UAVs. The authors in [258] considered the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Category**} & \multirow{2}{*}{**Specific Algorithm**} \\ \cline{3-3} \cline{5-5}  & & **CA** & & **ML** \\ \hline  
✓ & \(Q\)-learning \\ \hline
[253] & 2020 & & ✓ & DDPG \\ \hline
[28] & 2021 & ✓ & & DRL \\ \hline
[254] & 2020 & ✓ & & DQN \\ \hline
[255] & 2019 & ✓ & Fuzzy C-means \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of energy optimization techniques based on resource management. problem of EE maximization in a UAV-based relay system used for secure communication. The EE is maximized by jointly optimizing the transmission scheduling, power allocation, and UAV trajectory. The optimization problem was solved using an SCA-based iterative algorithm. The work in [259] studied a joint flight scheduling and resource allocation in a multi-UAV network to maximize the EE of the network. Two approaches, coordinated and uncoordinated, were proposed to solve the joint optimization problem. In the former, UAVs cooperate with each other to maximize network EE via information exchange, while, in the latter, each UAV maximizes its EE separately with no information exchange. Then, heuristic algorithms were proposed to obtain the optimal solution in each approach. The authors in [260] investigated scheduling of beaconing periods to minimize the energy consumption for UAV-based networks during emergency scenarios. The beaconing periods of the UAVs was modeled as a non-cooperative game and then a distributed learning algorithm was proposed to determine the Nash equilibrium of the game that would result in minimum energy consumption in the network. Similarly, the authors in [261] considered the problem of activity scheduling for UAV-BSs that are used to serve users in areas, which do not have cellular infrastructure, to maximize the EE of the UAVs. Then, a heuristic algorithm based on Nash bargaining theory (NBG) was proposed to determine the optimal beaconing period that maximizes the EE of each UAV-BS. In [262], the problem of multi-task cooperation in a multi-UAV system was investigated to minimize the energy consumption of the UAV. The task assignment problem was first modeled as a coalition formation game (CFG) and a heuristic algorithm was developed to solve the task allocation problem resulting in reduced energy consumption in the network.