## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches
Machine Learning Approaches  
The work in [223] considered the design of an energy-efficient distributed navigation system for a group of UAV-BSs for the provision of long-term coverage to ground users. The problem was first modeled as a distributed multi-agent control problem, then a deep RL (DRL) framework was developed to control each UAV while minimizing the total energy consumption of the UAVs and ensuring minimum coverage requirements and geographical fairness are achieved. An energy management strategy for solar-powered UAV-BSs based on deep \(Q\)-networks was proposed in [224], wherein the total energy consumption of the UAV-BSs as well as the 3D flight trajectory were jointly optimized to enhance their communication capacity. The authors in [225] studied the problem of 3D trajectory design and frequency allocation while considering the energy consumption of the UAV-BSs and the fairness of user coverage. In this regard, the energy consumption of the UAV-BS was first modeled as a function of the 3D movement of the UAV. After that, a DRL framework known as energy-efficient fair communication through trajectory design and band allocation (EEFC-TDBA), which is based on deep deterministic policy gradient (DDPG), was proposed to regulate the speed and direction of the UAV-BS as well as the frequency band allocation to maximize the EE, enabling the UAV-BS to arrive at its destination before the battery energy depletes and achieve fair user coverage. The problem of trajectory planning for multi-UAV system was considered in [226] to achieve energy-efficient user coverage. The multi-UAV trajectory planning problem was first modeled as two multi-agent cooperative games, followed by developing a decentralized multi-UAV cooperative RL (DMUCRL) framework based on \(Q\)-learning to find the equilibrium of the games. Using the proposed framework, the UAV-BSs can choose their optimal trajectory and the recharging schedule that would result in minimum energy consumption in the network. The performance evaluation showed that the proposed DMUCRL framework is able to enhance the energy efficiency of the network by 27% compared to straight flight trajectory planning. An ML framework for joint trajectory design and power control to maximize user sum rate in a multi-UAV wireless network was proposed in [227]. The solution procedure involves three stages. First, a multi-agent \(Q\)-learning algorithm was developed to find the optimal UAV positions using the initial positions of users. Second, exploiting real user mobility data set obtained from Twitter, an algorithm was developed to estimate future user locations based on echo state network. Finally, another multi-agent \(Q\)-learning algorithm was developed to forecast UAV positions in each time slot based on user mobility. The proposed method resulted in about 17% increase in the throughput of the network. The work in [228] investigated the problem of UAV trajectory optimization for post-disaster management while considering the availability of energy as constraint. To solve this problem, an energy-aware multi-armed bandit algorithm was proposed to determine the optimal trajectory that would maximize the number of ground users visited while respecting the energy constraints. Similarly, in [229], the authors studied the application of UAV-BS for the provision of wireless coverage following a natural disaster when the user distribution is not known. They aimed at finding the optimal path that would maximize user service request while considering the battery capacity of the UAV. The path selection problem was modeled as an extended multi-armed bandit problem and two algorithms: distance aware-upper confidence bound and \(\epsilon\)-exploration algorithm were proposed to determine most energy efficient path. Simulation results reveal that the proposed method outperforms baseline methods by 85% and 100%, respectively, with respect to the number of users served. The authors in [230] considered the problem of resource-aware data offloading in a MEC-based UAV swarm network.