# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 4 Resource Management and Network Planning
### Energy Efficiency and Power Control  
UAVs have limited capabilities in terms of energy resources, which makes the energy and power consumption optimization a very important factor of the overall network's performance [79]. In that sense, various ML solutions have been proposed that exploit in a near optimal manner the network's resources towards minimizing energy consumption. In Reference [80], a group of UAVs is considered to fly at a certain altitude that allows them to provide communication coverage for ground users in a specific region. The main objective of this paper is to achieve a certain communication coverage, while preserving the UAVs' connectivity and simultaneously minimizing their energy consumption. More specifically, a DRL method is proposed for controlling the UAVs in order to maximize the energy efficiency, while ensuring effective and fair communication coverage and network connectivity, learning and adapting to the wireless environment by relying on two powerful deep NNs. In the simulations, the proposed approach was compared to two conventional methods without ML capabilities. The first one, titled Random, randomly selected a moving direction and a flying distance for each UAV, while the second one, titled Greedy, selected the moving direction and flying distance in a sequential manner, maximizing the instantaneous reward for every UAV abiding to the region boundary and connectivity constraints. It was shown that the ML-based solution provided improved performance in terms of average coverage score, fairness index, average energy consumption, and energy efficiency. The study in Reference [81] presents a holistic framework for PSNs based on UAV deployment that communicates through NOMA and recharges the batteries of IoT ground nodes under the wireless powered communication (WPC) paradigm. The main motivation behind this work is to adopt ML in order to address the network lifetime maximization of PSNs and their lack pf centralized cellular connectivity, thus providing a distributed, autonomous, and resilient communication deployment. More specifically, each IoT node selects a specific role, either acting as a coalition head or coalition member in a distributed manner by adopting the MG theory. Moreover, communication can take place either between IoT nodes or with an UAV/evolved NodeB (eNB), while RL is employed to form coalitions of IoT nodes, considering energy consumption, in an autonomous fashion. Then, outlining the importance of the coalition heads operating as gateways for the rest of the IoT nodes, resource management optimization is performed targeting efficient RF energy harvesting, determining the UAV trajectory and uplink transmit power control towards energy-efficient transmissions. The performance of the proposed framework is evaluated via simulations, and it was observed that gains can be achieved in terms of energy efficiency, robustness, and scalability. In Reference [82], a similar system model to Reference [81] was assumed, in which UAVs are used to wirelessly charge a large number of energy-constrained IoT devices; ML can enhance the performance of energy transfer and sensor network communication. These devices are assumed to be randomly distributed in a two-dimensional area, and an UAV is responsible for transmitting energy through RF signals. Thus, the performance of the wireless power transfer and peer-to-peer sensor communication towards a fusion center was jointly considered. Aiming to enhance the performance of both aspects, an ESN-based scheme for sensor energy consumption prediction was adopted. Exploiting these predictions, the distributed sensor nodes were clustered using an improved k-means clustering algorithm under a specific rule to minimize packet losses. Furthermore, the distributed power control problem towards sensor interference mitigation was modeled as a mean field game (MFG), considering both the sensors' energy and their mutual interference.