## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Energy Optimization of UAV-Assisted Cellular Networks
#### 8.2.1 Conventional Approaches  
The authors in [107] considered EE in a UAV-assisted mmWave HetNet comprising macro BSs (MBSs), mmWave small BS (SBSs), and UAVs. A joint optimization of both the sub-carrier and power allocation was performed in order to maximize the EE of the network while considering minimum QoS and maximum transmit power as constraints. Two heuristic algorithms were developed to maximize the EE of the MBS and minimize the power consumption of the UAVs while satisfying minimum QoS requirements. In [106], the authors investigated the use of UAVs for maximizing the EE in a UAV-assisted small cell (SC) network by enabling the switch off of low EE BSs. A low-complexity algorithm was developed to determine the optimal positioning and sleeping strategy that would maximize the EE of the network. Similarly, the work in [105] considered the problem  
\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
[256] & 2018 & ✓ & & Heuristic \\ \hline
[257] & 2021 & ✓ & & Heuristic \\ \hline
[29] & 2020 & ✓ & & Heuristic \\ \hline
[258] & 2020 & ✓ & & Heuristic \\ \hline
[259] & 2020 & ✓ & & Heuristic \\ \hline
[260] & 2016 & ✓ & & Heuristic \\ \hline
[261] & 2018 & ✓ & & Heuristic \\ \hline
[262] & 2020 & ✓ & & Heuristic \\ \hline
[30] & 2020 & & ✓ & DDPG \\ \hline
[263;264] & 2020, 2021 & & ✓ & Actor-critic \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of energy optimization techniques based on flight and transmission scheduling.  
of energy optimization in UAV-assisted HetNets comprising MBSs, micro cells that can be switched off/on, and UAV SCs that are powered by solar energy. The optimization objective was to determine the optimal position of the UAVs as well as the off/on status of the micro cells that result in the minimum energy consumption in the network while respecting QoS constraints.  
Energy and hover time optimization in a three-tier UAV-based HetNet comprising UAVs, MBS, and SBSs was considered in [266] to enhance the EE of the network. A two-layered optimization framework was developed where the power consumption of each tier was first optimized in order to maximize the EE of the network while respecting QoS constraints, followed by the hover time optimization of the UAVs. A heuristic algorithm based on Lagrange multiplier and subgradient approach was proposed to maximize the EE of the network. The authors in [267; 268] proposed a weighted power allocation scheme for minimizing the total energy consumption of both UAVs and terrestrial BSs of a UAV-assisted HetNet while satisfying QoS constraints of mobile users. A heuristic algorithm was developed to determine the optimal power allocation strategy that would result in minimal energy consumption in the network.  
The authors in [269] considered the optimal placement of UAV mounted remote radio heads (D-RRH) in a CRAN to minimize the transmission power while ensuring that a maximum number of users are covered. The D-RRH placement problem was first decomposed into two: vertical and horizontal placement problem. The former was solved using Weiszfeld algorithm, while the latter was solved analytically. Energy efficient UAV deployment in UAV-assisted C-RAN with flexible functional split selection was investigated in [266]. To achieve the minimal energy consumption, both the horizontal and vertical locations as well as the coverage radius of each UAV were optimized using mathematical analysis. In addition, a closed-form expression to determine the upper and lower bound of the number of UAVs required to achieve optimal energy consumption was derived. Table 5 summarizes the CA and ML approaches that are applied for energy optimization in UAV-assisted cellular networks.