## 2 Related Work  
Some novel applications of UAVs in various domains are listed in [32]. Recently, UAVs have found applications in monitoring the stability of landfills, monitoring the frequency of settlements, and, consequently, safeguarding the people living near landfills from serious hazards [33]. Some studies have investigated the applicability of UAV-based photogrammetry to monitor geometric changes in landfills to provide precise information to make informed reclamation decisions [33]. Other works have proposed UAV-based antennas and propagation measurements for electromagnetic field assessments, as they are flexible, cost effective, and easy to deploy [34]. The authors in [34] also evaluated some \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline
3 GPP & Third generation partnership project \\
5G & Fifth generation (communication networks) \\
6G & Sixth generation (communication networks) \\ AoI & Age of information \\ BER & Bit error rate \\ BS & Base station \\ BSM & Basic safety messages \\ CNN & Convolutional neural network \\ CPM & Co-operative perception messages \\ C-V2X & Cellular vehicle-to-everything \\ DQN & Deep Q-network \\ DRL & Deep reinforcement learning \\ FDRL & Federated deep reinforcement learning \\ FedAvg & Federated averaging \\ FedSGD & Federated stochastic gradient descent \\ FL & Federated learning \\ FL-DDPG & FL-based dual deterministic policy gradient \\ FRL & Federated reinforcement learning \\ GNSS & Global navigation satellite system \\ GRU & Gated recurrent unit \\ i.i.d. & independent-and-identically-distributed \\ ITU-R & International Telecommunication Union \\ LoS & Line of sight \\ LSTM & long short-term memory \\ MADRL & Multi-agent deep reinforcement learning \\ MDP & Markov decision process \\ MEC & Mobile edge computing \\ ML & Machine learning \\ MSE & Mean square error \\ NLoS & Non-line of sight \\ NOMA & Non-orthogonal multiple access \\ NTNs & Non-terrestrial networks \\ OFDM & Orthogonal frequency division multiplexing \\ OFDMA & Orthogonal frequency division multiple access \\ OTFS & Orthogonal time–frequency space \\ QoS & Quality of service \\ RL & Reinforcement learning \\ RNN & Recurrent neural networks \\ RSSI & Received signal strength indicator \\ SCA & Successive convex approximation \\ SINR & Signal-to-interference-plus-noise ratio \\ SPS & Sensing-based semi-persistent scheduling \\ SNR & Signal-to-noise ratio \\ SQP & Successive quadratic programming \\ TDD & Time-delay-doppler \\ TTI & Transmission time interval \\ UAV & Unmanned aerial vehicle \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of the abbreviations used in this manuscript. channel models for UAV positioning accuracy and antenna alignment in the presence of large-scale antenna arrays. The authors reported that some inaccuracies in positioning were alleviated by virtue of the recent advances in portable measurement systems and antennas designed specifically for UAVs [34]. Moreover, some authors have hypothesized that as the UAV-assisted wireless channels are overt, the UAV communications are vulnerable to eavesdropping attacks [35]. The authors in [35] proposed novel solutions to enhance UAV communications' minimum secrecy rates. The authors formulated an optimization problem with parameters such as user association variables, UAV trajectory, and output power and proposed a solution based on sequential decision-making. Specifically, the work proposed and utilized a single agent soft actor critic and twin delayed deep deterministic policy gradient algorithm to jointly optimize the aforementioned parameters [35]. However, jointly optimizing the UAV trajectory and power consumption was not addressed by these works. In some recent works, it has been hypothesized that the UAVs do not operate accurately in indoor environments where the localization performance of global navigation satellite system (GNSS) is suboptimal [36].