## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches  
In [116], the authors considered the problem of energy efficient 3D placement of a UAV-BS for coverage maximization. The problem was first modeled as a circle placement problem and a heuristic algorithm was proposed to determine the optimal 3D location that maximizes the coverage area while minimizing the transmit power. The proposed technique outperforms the random deployment of UAV-BS throughout the whole coefficient of variation range (CoV). The suggested technique can provide coverage for up to 70 users at a transmit power of 25.5 dBm when the CoV is 6, whereas the random deployment approach only covers 22 users at a higher transmit power of 30 dBm. The work in [23] investigated the cost and energy optimization of a UAV-based communication network while considering both the communication and propulsion energy consumption. In this regard, a multi-level circle parking (MCP) algorithm was developed to determine the optimal 3D-hovering positions of the UAVs that maximizes both the uplink and downlink global EE of the network. In addition, the result of the optimal hovering positions obtained was used to determine the number of UAV-BSs and flight parameters required to minimize the total system cost. The authors in [24] proposed a deployment decision mechanism for optimizing the number and locations of UAV-BSs in a UAV-assisted vehicular network to maximize the communication coverage and minimize the energy consumption of the UAV-BSs. The proposed mechanism employs circle packing theory to determine the optimal positions of the UAV-BSs, while an energy optimization model was developed to minimize the power consumption of the UAV-BSs. The authors in [176] developed an analytic solution to determine the optimal altitude for a UAV-BS whereby the transmit power needed to provide coverage to a specific area is minimized. In [177], an EE maximization approach was proposed for a UAV-BS relay system to extend the battery life while maintaining network throughput. In the proposed approach, the hovering position, where the UAV-BS expends the least energy, is considered to be the optimal UAV-BS location, is determined via mathematical analysis, after which the power allocation was also optimized. The work in [178] considered the optimal positioning of a UAV-BS in order to maximize its EE with the altitude and minimum user data rate being constraints. The EE problem was formulated as a monotonic fractional optimization problem and solved using polyblock outer approximation algorithm. Two UAV location optimization algorithms were proposed in [179] to minimize the transmit power of the UAV-BS. The first algorithm assumes equal power allocation, while the second algorithm is based on successive convex approximation (SCA) and does not assume equal power allocation. The authors of [119] proposed a UAV-BS positioning algorithm based on Coulomb's law to maximize the EE of the UAV-BSs while considering interference between UAVBSs and user requirements. The algorithm presented in this article outperforms existing solutions by at least 2.5 times while improving customer satisfaction in terms of data rate by about 6% to 10%. An energy-aware 3D deployment algorithm based on Lagrangian and sub-gradient projection for optimal placement of the UAV-BSs was proposed in [180]. In [181], the authors developed a framework for optimizing the energy consumption of individual UAV-BSs in a multiple UAV-BSs network while carrying out location-specific tasks. The proposed framework uses order-K Markov predictor to estimate the task locations to enable proactive deployment of UAV-BSs and minimize their energy consumption. In addition, a heuristic algorithm was developed to place the UAV-BSs in their right locations as well as assign their respective tasks to them. The authors in [118] investigated the optimal 3D placement of a UAV-BS with a tilting antenna to provide sufficient coverage for ground users while utilizing minimum energy consumption.