## 1 Introduction  
Jamming is a phenomenon where undesired jamming signals disrupt a desired legitimate communication. The intensity of jamming cannot only be characterized by the power or the number of jamming devices, but also by the distribution of jammers. Clustered jamming refers to the jamming signals exploiting the clustered distribution of jammingdevices [1]. Conventionally clustered jamming consists of multiple clusters of jammers, and their distribution is modeled using the Matern cluster process (MCP). MCP consists of parent nodes forming the cluster centers and the child nodes or the jamming devices [2; 3]. The child nodes or the jamming devices are distributed using uniform distribution inside the cluster of a circular radius. The jamming clusters and the jammers in a cluster are Poisson random variables. The total number of jamming devices in the considered area is given by the total number of jammers in all the clusters. Jamming signals influence the effectiveness of vehicle-to-everything (V2X) communication in terms of vehicle equipment's coverage and spectral efficiency (SE) [4; 5]. V2X connectivity is critical for improving road safety and enabling vehicular autonomy. It provides safety-related features, including disaster cautions, pre-crash sensory notices, and automatic crash updates, that are critical for minimizing fatalities and enhancing global highway safety. Furthermore, V2X enables sophisticated autonomy capabilities such as vehicular platooning, joint collision minimization via vehicular communication, and automatic overtake for effective and reliable lane changes [6; 7]. Yet, jamming attempts may drastically influence such important features by interfering wirelessly, resulting in extended latency or missed signals, elevated accident potential hazards, and reduced productivity of vehicular technologies. Thus, analyzing the impact of jamming becomes essential in investigating vehicular network performance. To meet the severe requirements of self-driving applications, millimeter-wave band connection is a vital requirement for unmanned aerial vehicle (UAV)-assisted cellular-V2X (C-V2X) systems. Beam orientation is critical in the millimeter wave range to avoid propagation losses as well as to preserve a stable network. For aerial networks, line-of-sight (LOS) path information is critical for introducing beams, especially for flying links [8]. To gather and send data in the millimeter wave frequency band, directed beams must be aligned between sources and terminals. Beam tracing is the continuous monitoring of a beam targeting a subject that is moving [9]. Thus, detecting the beam alignment of automobiles that move with variable speeds is a greater challenge than detecting objects that are still. Beam tracing gets more difficult when the three-dimensional (3D) antenna's radiating beam varies due to wind, air pressure, or noise from the regulating motors. As a result, it is critical to quantify the variations of the UAVs' 3D radio beam, particularly for UAV-assisted C-V2X transmissions. Vehicular nodes (V-Ns), comprising vehicles, pedestrians, and roadside units (RSUs), can interact with each other either directly or using a sharing network layout [7; 10]. The third-generation partnership project (3 GPP), Release 16 enables V-Ns to communicate directly via the PC5 (sidelink) connection as well as collaboratively via the Uu connection. The shared technique design uses independent downstream links and upload links for the macro base station (MBS) or UAV. Normally, for C-V2X connectivity, the V-N's upward connectivity permits transfer to the telecom tower-mounted station, which might be an MBS (in V2M link) [11]. In contrast, for UAV-assisted C-V2X connectivity, the V-N's upstream link enables dissemination to the LOS low-altitude platform (LAP) (in V2L link) or non-LOS (NLOS) LAP (in V2N link).