## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches
The authors in [194] proposed an energy efficient transmission mechanism for UAV-enabled mmWave communication system with NOMA by jointly optimizing the UAV position, power allocation, and precoding in order to maximize user coverage and minimize the energy consumption of the UAVs. Due to the complexity of the optimization problem, it was first divided into three sub-problems, and three heuristic algorithms were designed to solve each problem iteratively. The authors of [186] proposed algorithmic optimization techniques for determining the optimal locations to place drones in order to cover designated targets in a cost-efficient manner. The proposed methodologies consider factors such as target locations, drone capabilities, and communication constraints to minimize the number of drones needed while maximizing target coverage. To address the problem of optimal drone placement and cost-efficient target coverage, the article proposes two mathematical models: an integer linear optimization model and a mixed integer non-linear optimization model. These models were used to formulate the problem mathematically and find the optimal solution that minimizes costs while maximizing target coverage. In another study, [195] proposed a two-phase process where the first phase involves optimal UAV placement and the second phase involves resource allocation to the UAVs. The optimization objectives include minimizing the total energy consumption and maximizing the network coverage and data rate. The approach utilizes a combination of linear programming techniques and heuristic algorithms to achieve the desired objectives. The authors of [196] proposed an energy-efficient framework for data gathering using multiple UAVs in WSN applications with deadlines, based on clustering techniques. The proposed framework aims to minimize energy consumption while meeting the application's deadline. The article uses the MILP approach and greedy heuristic solution methodology as part of the proposed solution for optimizing the data gathering problem in WSN applications using multiple UAVs. Machine Learning Approaches  
The authors of [197] proposed a proactive power control and positioning framework for UAV-BSs to minimize interference and enhance EE in multi-UAV systems. The proposed framework comprises both offline and online phases. In the former, a supervised learning algorithm (random forest) leverages historical data to build a mobility prediction model, while in the latter, the predicted user positions are exploited to determine the sleep/wake status of the DSCs proactively, while an unsupervised ML algorithm (\(k\)-means) is employed to update DSCs positions and regulate the power consumption. The proposed approach resulted in a 97.3% increase in energy efficiency compared with GA and utilizes 0.4% of the execution time of GA-based approach An energy-efficient multi-UAV deployment framework was proposed in [198] in order to maximize user coverage probability. An ellipse clustering algorithm was developed to determine the optimal hovering altitude of the UAV that would result in minimal transmit power while maintaining QoS constraints. The simulation result reveals that the proposed method produced about a 50% reduction in the transmit power of the UAVs. A predictive on-demand ML-based UAV deployment for minimizing both the communication and propulsion energy consumption was introduced in [199]. In this regard, a ML framework was developed, which uses Gaussian mixture model (GMM) and weighted expectation maximization (WEM) algorithm to forecast the network traffic congestion areas. Then, \(k\)-means algorithm was used to partition the service area of each UAV, after which a gradient-based algorithm was developed to determine the optimal location of the UAVs that results in minimum energy consumption. The performance evaluation reveals that the proposed ML framework is able to reduce the power consumption of the network by over 20% compared to non-ML methods.