## 1 Introduction  
Sixth-generation (6G) communication networks are increasingly being characterized by non-terrestrial networks (NTNs) that operate using a terrestrial gateway to enable communication between ground nodes and aerial base stations [1]. In unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) networks, UAVs can be used as aerial data centers to store vehicle routes, traffic alerts, and calculate collision probabilities [2]. However, the UAV power consumption varies due to random fluctuations in the vehicular data traffic [3]. Hence, in a transmission window, maximizing the UAV communication range is critical to boost network capacity and improve performance. To improve the data rate and energy efficiency, the UAV trajectory and vehicle data transmission strategies should be optimized jointly [4]. The third-generation partnership project (3 GPP) aims to develop 6G technologies and standards that integrate UAVs into terrestrial vehicular communications [5]. The 3 GPP Release 16 and beyond include specifications for C-V2X communication that consider the effects of mobility, dynamic channel conditions, and latency challenges [6]. Maintaining robust communication in scenarios with fading channels and frequent link adaptations is a prevailing challenge in UAV-assisted C-V2X communication. If UAV computing resources are intermittently unavailable, it causes interruptions, and the vehicle's sensor data can become outdated relatively quickly [7]. Furthermore, machine learning techniques are being explored to address C-V2X channel dynamics to address the time-sensitive nature of vehicular data. This is aimed at achieving wider coverage by UAV, as well as providing uninterrupted processing capability to vehicular data [8]. In this paper, the UAV-assisted C-V2X communication channel is modulated using orthogonal time-frequency space (OTFS) modulation to improve performance in scenarios with high mobility, non-line-of-sight (NLoS) conditions and multipath environment [9]. OTFS transforms the wireless channel into the delay-Doppler domain, where the instantaneous channel conditions appear to be stationary in high-mobility scenarios [10]. In the delay-Doppler domain, delay represents the time delay of the signal, and Doppler represents the rate of change of the signal frequency. UAVs can be utilized to improve the coverage range and communications performance of vehicles. Due to the high mobility, UAVs can provide on-demand services to vehicles by adequately overcoming geographical constraints [11]. However, maximizing the UAV available battery power is challenging and comprises a multi-agent, multi-objective, co-operative non-convex optimization problem [12]. Moreover, the UAV-assisted C-V2X environment and the UAV trajectory are typically unknown and time-varying [13]. Therefore, a co-operative UAV-assisted C-V2X communication scenario that maximizes the available battery power of the UAV and optimizes its trajectory while serving the maximum number of vehicles is investigated in this paper. A brief timeline depicting the amalgamation of wireless communication technologies with transportation systems is illustrated in Figure 1. Figure 1 also illustrates the gradual integration of UAVs in vehicular networks in 5G and 6G wireless communication paradigms. Note, each vehicle captures a different kind of data packet, leading to non-independently identically distributed (i.i.d.) and heterogeneous data, requiring significant computational and storage resources. Moreover, a detailed overview and the evolution timeline of the recent applications of machine learning techniques for performance enhancement of UAV communication frameworks can be found in [14; 15]. Existing works have investigated federated learning (FL) techniques to effectively improve the quality of service (QoS) in UAV-assisted vehicular communications [16].