### Related Works  
Since UAV-assisted wireless networking has attracted considerable attention due to the advantages mentioned above, in addition to the research works--which will be detailed later in this survey--there are also numerous survey papers available in the literature. In this section, we will present them and make a comprehensive comparison in terms of their content and focus. It is worth noting that, among many, we carefully select the ones that are most relevant and recent in order to avoid diverting the scope of the discussion and keep this survey paper up to date with the latest literature. The authors in [33] investigated how UAVs can be utilized in public safety communications, and they took the subject from the EE perspective. After making an extensive classification of UAVs in terms of application, altitude, and network, they proposed a UAV network architecture for public safety. Then, they highlighted the critical need for EE in UAVs, followed by various energy optimization techniques and optimization methodologies. The authors did not go into much detail about the optimization algorithms and how they work, but they briefly discussed various methods. A seminal paper in [13] presents a tutorial in UAV-assisted wireless networking. In particular, UAVs were first classified in terms of their wing types (e.g., fixed and rotary) and altitude (e.g., low and high). Then, UAV assistance in wireless networks was thoroughly discussed by considering their roles in the next generations of wireless networks under different scenarios, including UAVs as flying BSs and UAVs as flying user equipment. Artificial intelligence (AI) integration to UAV-assisted wireless networking was analyzed in [34] with brief tutorials for different ML algorithms from different categories, such as supervised, unsupervised, and reinforcement learning (RL). Intelligent reflective surfaces (IRS) combined with UAVs were discussed, as well as how RL can be utilized for optimization purposes. Federated learning (FL), its advantages, and its applications to UAV-assisted wireless communications were also included with a detailed discussion. A similar survey paper in [16] also revealed how ML would help make UAV-assisted communications more effective. Starting with the introduction of the characteristics of UAVs, AI, and ML, the authors grouped the role of ML for UAV-assisted networks into four different categories: namely, physical layer aspects, resource management, positioning, and security. Various subjects, such as interference management, data caching, jamming, and mobility, were discussed separately under each category with an ML focus. Albeit not discussed from a wireless communication perspective, the survey paper in [35] focuses on deep learning (DL)--a sophisticated version of ML--applications for UAV systems. The authors extensively reviewed DL algorithms and their uses in UAV systems for different applications, including motion control, situational awareness, and path planning for search and rescue missions. The authors concluded their work with the challenges in both DL and UAV autonomy. The short review paper in [36] tried to reiterate the importance of self-organization (for a detailed discussion on self-organization in cellular networks, the readers are referred to [37]) in UAV-assisted connectivity. The scalability issue in UAV-assisted networking was raised, and the role of distributed algorithms in mitigating such issues was also discussed. The survey in [18]--being the closest one to our survey--mainly investigated the concept of green networking for UAV-assisted 6G networks. Different approaches in green UAV communications, including energy saving, energy harvesting, and RIS-assistance, were presented, followed by the enablers of green UAV communications. The functionality of UAV-assisted green 6G networks were also analyzed, and new research directions aimed at making the UAV-assisted 6G networks more feasible were identified. The authors in [19] provided a detailed survey in terms of UAV cellular communications.