## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches
Their goal is to select the optimal energy and processing-optimized path between the UAVs that would complete a data offloading task. A multi-armed bandit-based data offload path selection mechanism was proposed to ensure the survival of the selected path even when the size of the data to be transmitted is very large. The authors of [231] proposed an intelligent and energy-efficient framework to learn the optimal multi-UAV trajectory planning and data offloading strategy to ensure information freshness in delay-sensitive applications. A multi-level DRL framework known as decomposed multi-agent DDPG (DE-MADDPG), which Combines DDPG and MADDPG, was developed to learn the optimal collaborative UAV control policy that would significantly minimize energy consumption and ensure global information freshness in a dynamic changing environment. Simulation results show that the proposed DE-MADDPG framework outperforms the DDPG and MADDPG approaches by 1.7% and 4.6% in terms of total reward obtained as a result of UAV cooperation. The authors in [232] considered the problem of optimal path planning for a single UAV-BS as well as a fleet of UAV-BSs to enhance user coverage while respecting energy constraints and minimizing collisions between UAV-BSs. An RL-based approach was proposed for the single UAV-BS case while a DRL-based approach was proposed for the multiple UAV-BSs scenarios. The proposed methods exceeded the performance of benchmark methods (greedy and random method) with respect to total network coverage, the number of users covered, and system fairness by an average of 15.04%, 42.54%, and 38.94%, respectively, while respecting energy constraints. The authors in [233] investigated the problem of 3D UAV-BSs trajectory design to enhance the energy efficiency of the network while satisfying the QoS requirements of ground users. To achieve their goal, the energy-efficient trajectory design problem was modeled as a multi-armed bandit and solved using an algorithm that is based on the upper confidence bound. The performance evaluation shows that the proposed approach resulted in a 95% and 33.85% increase in the energy efficiency and throughput of the network, respectively, compared to the \(Q\)-learning-based approach. The authors of [234] investigated the problem of energy optimization of UAV-BSs for data collection application via a trajectory optimization technique. A \(Q\)-learning-based algorithm was proposed to determine the optimal trajectory selection strategy that will minimize the energy consumption of the UAV while achieving coverage targets. An ML-based framework was introduced in [235] to jointly optimize the multi-cast and trajectory for EE enhancement in UAV-based multi-cast networks. A combination of support vector regression (SVR) and \(k\)-means algorithm was first proposed to determine the optimal multicast grouping, after which a centroid adjustable traveling salesman-based algorithm was proposed to determine the optimal trajectory to maximize the energy-saving performance of the UAV. A joint trajectory and power optimization scheme based on DRL was proposed in [236] to maximize both the EE and throughput in a UAV-based communication system. The proposed scheme employs DDPG to solve the optimization problem in order to achieve the desired objective. The proposed technique is able to maximize the flight speed of the UAVs by 14% and enhance the throughput by 10% compared to the random method. The CA and ML approaches that are applied for UAV-BS path planning and trajectory design in order to minimize its energy consumption are summarized in Table 2.