## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches
#### 8.1.3 Resource Management
Then, a learning algorithm based on binary log-linear was proposed to find the equilibrium point in terms of coverage and EE maximization. A resource allocation framework involving the joint optimization of time and power resources was proposed in [247] to maximize the EE and throughput in NOMA based UAV network. To solve the optimization problem, a two-phase heuristic algorithm was designed: in the first phase, the optimal power allocation strategy required to maximize the EE is determined while the optimal time resources to enhance the throughput of all users are determined in the second phase. In [27], the authors examined UAV-assisted traffic offloading for EE enhancement in UAV-aided cellular networks. Their aim was to maximize the UAV-BS's EE through optimal resource allocation, trajectory design, and user scheduling. An iterative algorithm based on BCD was developed to solve the joint optimization problem. The authors in [242] proposed a joint optimization of the power allocation and trajectory design to maximize the secrecy average rate in UAV-based secure communications. An iterative algorithm based on BCD, CCP, and ADMM was developed to solve the joint optimization problem. An energy-efficient resource allocation framework was proposed in [248] to optimize computation offloading while ensuring the minimum energy consumption from the UAV. To achieve their target, a joint optimization of the UAV trajectory, computation task, and user transmission power was carried out, which is then solved by a heuristic algorithm based on SCA and Dinkelback. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**Category**} & \multirow{2}{*}{**Specific Algorithm**} \\ \cline{3-3} \cline{5-5}  & & **CA** & & **ML** \\ \hline  
A, MILP \\ \hline
[222] & 2018 & ✓ & & LP, Convex Optimization \\ \hline
[223] & 2020 & & ✓ & RL \\ \hline
[224] & 2021 & & ✓ & DQN \\ \hline
[225] & 2020 & & ✓ & DDPG \\ \hline
[226] & 2021 & & ✓ & Q-learning \\ \hline
[227] & 2019 & ✓ & Q-learning \\ \hline
[231] & 2020 & ✓ & DRL \\ \hline
[232] & 2020 & ✓ & RL, DRL \\ \hline
[234] & 2021 & ✓ & Q-learning \\ \hline
[235] & 2019 & ✓ & SVR, \(k\)-means \\ \hline
[236] & 2021 & ✓ & DDPG \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of energy optimization techniques based on path planning and trajectory design. In [249], the authors addressed the energy-efficient resource allocation problem in a two-hop uplink communication for Space-Air-Ground Internet of remote things (SAG-IoRT) networks assisted with unmanned aerial vehicle (UAV) relays. The goal is to maximize system energy efficiency by jointly optimizing sub-channel selection, uplink transmission power control, and UAV relays deployment. An iterative algorithm that combines two sub-problems is proposed for decomposition and convex approximation methodologies. Numerical results show that the proposed algorithm outperforms the benchmark scheme by at least 21.9% in terms of system energy efficiency. In another study [250], the authors worked on an energy-efficient multi-UAV-assisted multi-access edge computing (MEC) system where mobile devices offload computation-intensive and delay-sensitive tasks to UAVs equipped with MEC servers. However, limited energy budgets at UAVs and task completion deadlines make it challenging to determine device association and task offloading. To solve this, the paper proposed a joint optimization problem for device association, task assignment, and computing resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem. The problem is decomposed into three subproblems and solved using an iterative block coordinate descent (BCD) algorithm, which outperforms the benchmark schemes in simulations. In [251], the authors proposed a multicriterion framework for energy-efficient radio resource management (RRM) in a cooperative Internet-of-Things (IoT) network using UAV-based relays with radiofrequency (RF) energy harvesting.