## 2 Related Work
A low-complexity suboptimal algorithm was proposed to extract the probable correlations between trajectory alterations and operations that are difficult due to their complexity. To minimize the distance between vehicles, a DRL algorithm using actor-critic was proposed for the stochastic scheduling of MECs [47]. The authors in [10] demonstrated that due to the high-speed movement of UAVs, the estimation and equalization of wireless channels for over-the-air communication are complex tasks. However, it is possible to directly modulate data in the time-delay-Doppler (TDD) domain over a wide range of time frequencies in a multipath propagation channel using OTFS. In high-speed mobile communication systems, OTFS achieves greater diversity gain by adapting to time-varying channels and converting the multipath channel into the TDD domain [48]. Moreover, NLoS signal reflections and multipath propagation cause challenging channel conditions to achieve BER performance for high-spectral-efficiency signals. It was determined that the BER varied depending on the severity of the Doppler spread, SNR, and noise and interference in the channel. Moreover, the choice of modulation scheme within the OTFS framework was also a critical factor [49]. To maximize the network throughput, conventional DRL implementations rely on centralized data collection at the MEC server, which adapts UAV trajectories jointly during each time step. In order to adapt the UAV trajectory, the vehicle scheduling, and energy harvesting policies, a DQN approach was proposed to overcome the excessive communication and training overhead [50]. It was proposed to use the multi-agent DQN method to optimize the real-time downlink capacity for all vehicles, as well as the MADDPG method to assign targets and plan the trajectory of multiple UAVs to gather sensor data. The UAV speed control was adjusted according to its energy status and position using the Q-learning algorithm. Other approaches employed an actor-critic DDPG for jointly optimizing the UAV trajectories and transmission scheduling strategy using the k-means clustering to aggregate different vehicles. In order to minimize the age of information (AoI), actor-critic DRL was used to adapt the sensing and flying decisions of each UAV, and to optimize the UAV-vehicle association strategy and minimize the total flight time [51]. Using DRL in applications with random task arrivals to optimize resource distribution, many approaches have proposed a dynamic scheduling strategy. Using the FL algorithm, collaborative model training can be accomplished without sharing data [52]. In this model, the spatial correlation of the traffic of adjacent vehicles is extracted from the wireless traffic data as images. In order to improve the prediction accuracy, this model employs temporal aggregation to capture the relationship between wireless traffic during current time slots and traffic during upcoming time slots. To reduce the power consumption of the UAV, select multiple starting positions at random to mitigate the impact of the initial positions [53]. In recent machine learning approaches, spatio-temporal neural networks have been used for accurate cellular traffic forecasting, and variations combine the output with past statistical data in order to improve long-term prediction accuracy [54]. An LSTM-based architecture for cellular traffic prediction combines Gaussian process regression with LSTM to capture spatial and temporal dependencies. For extracting dominant periodic components, LSTM is used for learning the long-term relationships among small random values, and FL is applied to estimate the residual random values [55]. The FL methods were shown to have lower computational complexity than probability models to predict the baseline component and maximum likelihood estimation methods to estimate the residual component.