## 2 Related Work
This makes it even more difficult to accurately localize UAVs in dynamic environments with weak signal strengths in mission-critical applications and where channel interference is not negligible; hence, some alternatives to GNSS-based positioning were explored in [36]. These solutions estimate the distance and position using the received signal strength indicator (RSSI) based on Bluetooth low-energy beacons. The authors in [37] proposed a single-agent deep reinforcement learning approach to solve scheduling requests, and a short-term memory was constructed to forecast the traffic. Using echo state networks, a machine learning framework was proposed to predict packet distribution and traffic patterns to optimize UAV flight paths and cached content. The energy consumption of the hybrid online offload framework was minimized by using a deep learning-based hybrid memory to store offloading decisions [38]. In order to implement optimization methods, it was necessary to have complete information about how vehicles distribute their data and how traffic is distributed. The authors in [39] analyzed UAV placement strategies to maximize the number of vehicles under coverage to maximize the sum of channel gains or minimize non-convex path loss functions. The Dinkelbach algorithm and successive convex approximation (SCA) were used to maximize the UAV's energy efficiency by optimizing its trajectory, transmit power, and computational load distribution. In order to minimize overall energy consumption, the authors optimized flight trajectory, transmission power, time slot scheduling, and task data assignment using Lagrangian duality. To optimize both the UAV position and its computing resources simultaneously, a three-stage iterative method was proposed. Based on ideal LoS channels, independent of multipath channels and Doppler spread, a mobile edge computing (MEC) network was designed with access to base stations (BSs) and UAVs [40]. Moreover, in the existing works, the UAV-assisted C-V2X communications can be modulated using non-orthogonal multiple access (NOMA), orthogonal frequency division multiplexing (OFDM), or OTFS modulation schemes. * In NOMA, multiple vehicles share the same time and frequency resources, and their signals are distinguished by different power levels. Vehicles with stronger channel conditions are assigned lower power, while vehicles with weaker channel conditions are assigned higher power [41]. This leads to the efficient utilization of the available resources and enhances the throughput [42]. * OFDM divides the spectrum into multiple orthogonal subcarriers in the frequency domain and uses them to transmit data simultaneously. However, OFDM faces challenges in high-mobility environments, where Doppler spread is significant and the performance degrades in NLoS conditions and multipath environments [43]. * OTFS modulation improves communication performance in scenarios with high mobility, NLoS conditions, and multipath environments [9]. OTFS transforms the wireless channel into a new domain called the delay-Doppler domain, where the instantaneous channel conditions appear to be stationary in high-mobility scenarios [10]. A multi-hop UAV-assisted relay network was employed to facilitate communication between transceivers on the ground by optimizing the channel assignment and flight control of the UAVs [44]. To maximize computation capability, the UAV deployment coordinates and altitude, transmit power, and bandwidth allocation were optimized together with the resource allocation and deployment strategy of the UAVs. By optimizing the communication scheduling, the UAV trajectory, and the computing resources jointly, subject to mobility, connection, and computation constraints, the authors proved that it could lower the signal-to-noise ratio (SNR) based on the first-order Gauss-Markov process and maximize computation capability [45]. An orthogonal frequency division multiple access (OFDMA) multi-user MEC with resource allocation was studied in [46].