# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 1 Introduction
### Related Work  
Vehicle-to-vehicle (V2V) communications has emerged as a critical area of research, recognized for its capacity to significantly improve road safety and traffic flow [17; 18; 19]. The literature is replete with studies investigating various facets of this domain. A prominent focus among these studies is the comprehensive examination and comparison of mmWave versus Wi-Fi communication technologies, assessing them across a spectrum of metrics such as signal-to-noise Ratio (SNR), bit error rate (BER), average throughput, packet loss rate (PLT), and latency, among others. In this vein, ref. [20] conducted an in-depth comparative study of IEEE 802.11p and mmWave technologies within V2V contexts. Their analysis spans several critical factors, including antenna configurations, inter-vehicle distances, frequency bands, and beam alignment techniques. The comparison leverages key performance indicators like path loss, line-of-sight (LoS) probability, data rate, and outage probability. The results underscore IEEE 802.11p's provision of stable and reliable connectivity over short to medium distances. In contrast, mmWave technology facilitates high-throughput connections but suffers from greater instability due to its propagation characteristics and the stringent requirement for precise beam alignment. For example, ref. [21] explores the efficacy of IEEE 802.11p, LTE, and 5G technologies within vehicular communication frameworks, specifically focusing on the exchange of real-time road weather and observation data in both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) contexts. Their evaluation employs metrics such as throughput, packet loss percentage, and latency to gauge performance. The study highlights 5G's superiority in reducing packet loss and achieving ultra-low latency compared to IEEE 802.11p and LTE, with LTE showing moderate effectiveness and IEEE 802.11p being suitable for limited operational contexts due to its coverage constraints. This research points towards the potential of enhancing vehicular communication systems for safety applications through the integration of a heterogeneous network that amalgamates various technologies. Additionally, addressing the blockage effect constitutes a critical dimension in V2V communication analysis. The modeling of V2V systems often employs 1D and 2D frameworks, with point process theory and stochastic geometry being instrumental in numerous studies on this topic [22; 23; 24; 25; 26]. As an illustration, the work of [23] conducts a coverage analysis in vehicular networks, employing a 1D homogeneous Poisson point process (PPP) to represent vehicle locations at a specified density and a Poisson line process (PLP) for road modeling. This study elucidates the effects of node and road densities on network coverage, tackling the complexities of calculating interference and the distances between transmitters and receivers. Nevertheless, it primarily concentrates on the likelihood of coverage, sidelining other vital performance indicators and network behaviors such as transmission probability, latency, handover processes, and interference control, which are crucial for a comprehensive evaluation of vehicular communication systems' dependability. Conversely, ref. [24] addresses a 2D network scenario, applying stochastic geometry to examine vehicular networks within orthogonal road layouts, conceptualized as a 2D Poisson bipolar network. This approach facilitates the derivation of success probabilities, accentuating the significance of cross-road interference on network dependability. Similarly, ref. [25] proposes an obstacle-influenced channel model tailored for V2V communications. This model, based on empirical dual-slope path loss measurements and a two-state Markov Chain, precisely captures the influence of dynamic obstacles on communication channels, integrating a Poisson process to mimic vehicular traffic and the alternating conditions between obstructed and clear line-of-sight caused by moving obstacles. Furthermore, ref.