## 1 Introduction  
As autonomous driving technologies evolve and application services become more advanced, autonomous vehicles generate more and more wireless data, which results in a huge strain on vehicle networks [1]. Hence, vehicle communication technologies are receiving great attention from both industry and academia [2; 3]. The integration of 5G technology in vehicular communication systems aims to enhance the capabilities of connected vehicles and enable various advanced applications to improve road safety, traffic efficiency, and overall transportation systems [4; 5]. One of the critical requirements for 5G vehicular communications is low latency. Researchers are working on developing communication protocols and technologies to minimize latency, ensuring quick and reliable data exchange between vehicles and infrastructure. In order to provide low latency and high data service to vehicles on the road, the integration of edge computing in 5G vehicular networks is gaining attention. Edge computing brings computational resources closer to the edge of the networks, such as base stations or road-side units (RSUs) [6; 7]. Through multi-access edge computing (MEC), vehicles can simultaneously offload their tasks to the edge computing servers and obtain high-speed computing services. The authors of [6] proposed low-latency offloading scheduling for dependent tasks in MEC-enabled 5G vehicular networks. The authors of [7] developed a deep reinforcement learning (RL)-based non-orthogonal multiple access (NOMA)-assisted secure offloading for vehicular edge computing (VEC) networks in the presence of multiple malicious eavesdropper vehicles. The deployment of 5G vehicular communications will deliver higher data rates, lower latency, higher reliability, and more devices to support a variety of intelligent transportation systems (ITS). Research on 5G-enabled ITS has been a major research area. In [8], 5G technologies for the quality of user experience in vehicular networks were explored. The authors of [9] provided a comprehensive overview of research on 5G vehicular applications,communication, and computing. The 5G vehicular communications leverage advanced antenna technologies like beamforming and massive multiple-input multiple-output (MIMO), which can improve the communication efficiency between vehicles and the infrastructure by focusing the signals directionally and increasing the spectral efficiency. The MIMO antenna design is important to apply MIMO technologies to vehicle communications. The authors of [10] presented a comprehensive review of MIMO antenna design approaches for 5G and beyond. The authors of [11; 12] designed and developed antenna radiators for the sub-6 GHz 5G frequency band. Recently, the Internet of vehicles (IoV) have become a key enabler for future smart and connected transportation systems to create an intelligent and interconnected transportation ecosystem. Hence, it is necessary to develop an IoV antenna that can cover all the frequency bands for the long-term evolution (LTE) and the mid-band 5G systems. The authors of [13] designed a vehicular antenna by modifying the design of the Vivaldi antenna to support a broadband frequency range. In [14], 5G leverages millimeter-wave (mmWave) frequencies to provide high data rate services with higher bandwidths and accommodates a greater number of simultaneous connections. However, mmWave vehicle communications are more susceptible to blockage by other vehicles and also suffer from higher path loss due to vehicles' high mobility. Hence, highly directional antenna beamforming technologies are required to mitigate the path loss effects of mmWave links. The main lobe generated by directional beamforming generally has a narrower beamwidth, which concentrates the transmitted power in a specific direction, mitigating some of the propagation challenges associated with mmWave frequencies [15]. In addition, directional beamforming improves the signal-to-noise ratio (SNR) at the receiver by reducing interference from other beams [16].