## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches
To achieve this objective, two heuristic algorithms were proposed to find the optimal energy allocation strategy among the trajectories that would result in minimum energy consumption in the network. In [215], the authors considered energy minimization in a UAV-based network with multiple eavesdroppers. The energy minimization problem was first modeled as a mixed integer optimization problem involving the UAV trajectory and user scheduling while maintaining QoS constraints. Then, an iterative algorithm based on BCD and SCA was proposed to find the optimal solution that results in minimum energy consumption in the network. An EE framework via trajectory optimization in a fixed-wing UAV--used for data collection and forwarding between ground nodes--was proposed in [216] to minimize the energy consumption while respecting throughput constraints. A heuristic algorithm based on SCA was formulated to determine the optimal strategy that would result in minimum propulsion energy consumption. The work in [217] studied EE and throughput maximization in UAV-enabled vehicular networks via joint optimization of the UAV trajectory design and power allocation. The joint optimization problem was first divided into two, and then a heuristic algorithm based on SCA was developed to simultaneously determine the optimal trajectory and power allocation strategy in each iteration. In addition, an EE optimization algorithm was developed based on fractional programming and sequential optimization. In [218], the authors considered the joint optimization of UAV trajectory, velocity, acceleration, and transmit power to maximize the EE while maintaining the minimum user rate. An iterative algorithm based on SCA was proposed to determine the optimal solution of the joint optimization problem. A multi-UAV cooperative mechanism was proposed in [219] to maximize the EE by the joint optimization of the UAV trajectory and transmission power. An iterative algorithm based on BCD, SCA, and Dinkelbach method was employed to determine the joint EE maximization strategy. The work in [220] considered the problem of minimizing of the energy consumption in UAV-assisted relays. To achieve minimal energy consumption in the UAV, the transmit power, UAV trajectory, and time slots were jointly optimized using an iterative algorithm that is based on BCD and SCA techniques. In another study [142], the authors proposed three algorithms for assigning energy-efficient trajectories to a fleet of UAVs, with a focus on multi-UAV path planning and assignment. These algorithms include an optimal path planning solution using MILP, as well as two greedy heuristic solutions. The study also incorporates the concept of space discretization for collision avoidance and examines the trade-offs between energy consumption and complexity. The work in [221] developed a new scheme for quality-aware and energy-efficient coverage and path planning for networks of UAVs in geometrically complex environments. The scheme includes an occlusion-aware waypoint generation algorithm, a vehicle routing problem formulation to assign waypoints to multiple UAVs within a global deadline, and a Min-Max energy path planning algorithm. Evaluation results show that the proposed scheme outperforms traditional techniques in terms of coverage and energy consumption. The methods used in the scheme include MILP techniques and genetic algorithms. In [222], the authors conducted a UAV-enabled communication system to collect information from distributed ground terminals (GTs) while maximizing the energy efficiency of the UAV. A mixed-integer nonconvex optimization problem is formulated and divided into two subproblems for easier solving. An efficient algorithm (based on linear programming and convex optimization) is also proposed to optimize GT scheduling and the UAV's trajectory. The proposed design outperforms two benchmark designs according to numerical results.