## 9 Enabling Technologies for Energy Efficiency in UAV-Based Cellular Networks
### Mobile Edge Computing (MEC)/Cloud  
MEC can prolong the operational time of UAVs by offloading some computing tasks from the UAV. The concept of allowing UAVs to offload computational tasks to MECservers was studied in [281], where it was shown to improve both EE and processing time for assigned tasks. Energy saving for UAVs through computational tasks offloading to MECs was extended to the case of multiple UAVs in [255], where the authors proposed a system to minimize both the computation, communication, and mechanical (due to flight or hovering) energy consumption of UAVs subject to latency and coverage constraints. The technical challenges that must be overcome to actualize UAV-based MEC include UAV trajectory/position planning [282; 283; 248], allocation of computational loads [283] in the case of multiple UAV/MEC servers, user transmit power/interference management [284], security against eavesdropping and jamming [285; 286], etc., while minimizing the energy consumption of the UAV (and in some cases that of the users [283]) so as to prolong its operational life. The authors in [287] proposed a time division multiple access (TDMA)-based workflow scheduling model to improve UAV EE while offloading computations to MEC servers by optimizing user association, computing resource allocation, hovering duration, and service sequences of users. Offloading computing tasks to UAV MECs and switching off computational elements was shown to lower energy consumption and job loss in [288], wherein the authors implemented an RL model to manage a framework responsible for switching computing elements in order to lower power consumption, delay, and job loss.  
The common architecture in UAV-based MEC systems involves tethering the MEC server to the UAV. In this way, the UAV provides coverage and connectivity to the users on ground, while all computational tasks are offloaded to the MEC server. This scheme was implemented in [289], where the UAV also provided wireless energy harvesting services to the ground users in addition to communication and computation coverage. Similarly, it was shown in [290] that, instead of serving just as a flying BS or mobile relay for ground-based users, the UAV can function as a mobile cloudlet to run compute-intensive applications for the users. This is achieved by offloading such tasks to a cloudlet attached to the UAV, thus minimizing the overall mobile energy consumption without reducing the QoS requirements of the target applications.  
Some of the articles highlighted above have adopted state-of-the-art ML algorithms to improve EE for UAV-enabled MEC networks. A few examples include the work in [282] where the long short-term memory (LSTM) algorithm was employed to predict computational tasks from the users and thereby direct MEC computational resources accordingly, and in [286] where RL was employed to plan an allocation strategy for the computational tasks, whereas a transfer learning algorithm builds on the allocation strategy to achieve faster computation. Similarly, a cooperative UAV MEC system was implemented in [288], where a UAV can offload computation to nearby UAVs to conserve energy and reduce latency. A RL algorithm was employed to assist a system controller in allocating the tasks to be completed to the available UAVs to improve target metrics of EE, latency, and job loss.