## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches
#### 8.1.3 Resource Management  
In this part, we review various resource management techniques involving bandwidth and power allocation and control methods that have been developed using conventional and ML algorithms to minimize the energy consumption of UAV-BSs. Conventional Approaches  
The authors of [237] proposed an energy-efficient multi-UAV deployment framework to maximize the coverage of a UAV network based on the game theory. The optimization problem was first divided into two parts: maximizing the coverage and power control with both being potential games. Then, a multi-UAV deployment algorithm based on spatial adaptive play was developed to tackle both power control and coverage maximization. An energy-efficient power allocation scheme for UAV-enabled spatial NOMA was proposed in [238]. The EE maximization problem comprising the power consumption, signal capacity, and spatial gain was formulated, after which a heuristic algorithm was developed to determine the optimal power allocation strategy. In [239], a resource allocation scheme, comprising user scheduling and power allocation in order to maximize the EE of a NOMA UAV networks under imperfect channel state information, was introduced. The proposed scheme was implemented using a heuristic algorithm for user scheduling to match the user to their sub-channels and a power allocation algorithm based on SCA was proposed to maximize the EE of the network. The work in [240] considered the joint resource allocation and trajectory optimization in order to enhance the EE in multi-UAV-based networks. An iterative heuristic algorithm based on SCA was proposed to solve the optimization problem. A joint power allocation and trajectory design mechanism for EE optimization in UAV-based relay systems was proposed in [241]. The optimal power allocation strategy was determined using a heuristic algorithm based on Lagrange multiplier method while the optimal trajectory design was determined using SCA. Then, an iterative algorithm which combined both algorithms was also proposed. The authors in [242] exploited UAV for confidential information communications in the presence of eavesdroppers. To achieve this, a power splitting mechanism was proposed to enable the UAV to transmit both confidential information and artificial noise at the same time. A joint optimization problem comprising the transmit power levels, power splitting ratios, and UAV trajectory was formulated and solved using an iterative algorithm based on BCD, concave convex procedure (CCP), and alternating direction method of multipliers (ADMM). The work in [243] considered maximizing the EE of a UAV-based secure communication system via the joint optimization of resource allocation and trajectory design. An iterative algorithm was developed to determine the efficient sub-optimal strategy. In [244], the problem of power minimization in an RIS-assisted UAV network was considered by jointly optimizing the UAV resource allocation mechanism and flight path with the help of an alternating heuristic algorithm. The authors of [245] examined the problem of power allocation in a multi-UAV based secure network while considering both small- and large-scale channel fading. Their objective was to maximize the throughput with transmission power, total energy consumption due to transmission of each UAV, and the duration of transmission as constraints. An analytical model was first developed for the throughput after which a heuristic algorithm was developed to solve the optimization problem. A joint optimization of the UAV deployment and power allocation was performed in [246] to maximize the area of service coverage and enhance EE. The optimization problem was decomposed into two separate problems involving the determination of the optimal transmit power control and 3D location. Using game theory, a utility function was developed to model the cooperation between both problems, which was proven to have Nash equilibrium points.