## 9 Enabling Technologies for Energy Efficiency in UAV-Based Cellular Networks
### Energy Harvesting Technologies  
Energy harvesting has become a hot topic among the scientific research community. As more and more power-hungry internet-connected devices are deployed in the IoT era, finding enough power sources to feed them is becoming a technical as well as an environmental problem. In addition, since most wireless devices are mobile, using stationary cables to recharge their batteries limits their mobility and therefore, their utility [304]. However, there are numerous energy sources in the ambient environment that can be harnessed to provide power to these devices to keep them operational for very long periods of time (if carefully designed, some devices can harvest enough ambient power to potentially run perpetually.) Popular energy harvesting schemes include harvesting energy from the electromagnetic fields, solar energy from the sun using photovoltaic cells, mechanical energy due to vibration or motion of the UAV, thermal energy, optical energy using laser beams [304],etc. In electromagnetic waves-based energy harvesting (e.g., RF energy harvesting), the transmitter covers some of the direct current (DC) from its battery into the RF energy to be transmitted. At the receiver, the received RF energy is converted back to DC and amplified using matching and rectifier circuits [305], then used to charge the on-board battery or directly used to power decoding or other communications operation. The efficiency of these RF-DC conversions significantly affects the amount of energy harvested. Regarding solar-powered UAVS, the authors in the study [306] solved the problem of short battery life in multi-solar-based UAVs that are used for coverage route planning (CPP). They measure the energy flow efficiency to assess how well the UAV consumes energy while in operation. Moreover, they also proposed a model for coverage path optimization for both regions with and without obstacles based on a graph search approach. Similarly, the authors in [307] proposed a multi-objective genetic algorithm-based framework for a solar-powered UAV. A multiple objective optimization model comprising remaining battery charge, the electricity used from the battery, and the energy harvested by the UAVs was formulated. The performance evaluation revealed that the proposed method was able to increase the maximum energy output of the photovoltaic cells by 5.79%, output electricity from the battery by 7.00%, and the state of charge of the battery by 10.94% compared to baseline methods for different flight trajectories. In [308], the energy efficiency of a solar-powered UAV-based network was designed to serve multiple ground users (GUs) via uplink data transfer was investigated. The study explored the effect of the UAV's transmission power, velocity acceleration, and heading angle on the uplink data rate and the harvested solar energy. A heuristic algorithm was proposed to improve the UAV's velocity acceleration and heading angle in order to maximize the system's energy efficiency. In [298], a UAV scavenges RF energy from a transmitting source (such as an eNodeB) to partially power the energy used in communication to enable it to serve as a relay to forward data between the source and user terminals on the ground. The energy harvesting is based on the time-splitting technique in which the UAV harvests energy in one time slot and performs communication in another slot. A similar energy harvesting technique for UAV-supported communication was explored in [309], wherein a power-splitting technique was used instead of the time-splitting scheme adopted in [298]. In power-splitting, the energy harvesting unit (UAV in this case) has separate hardware for handling communication and energy harvesting, so that both can be performed simultaneously. In addition, the authors in [310] derived an optimal strategy for allocating charging time and power to UAVs to wirelessly charge them during operation. The charging power is supplied by the BS, i.e., it is scavenged from the RF energy typically used for communication.