In vehicle-to-all (V2X) communications, roadside units (RSUs) play an essential role in connecting various network devices. In some cases, users may not be well-served by RSUs due to congestion, attenuation, or interference. In these cases, vehicular relays associated with RSUs can be used to serve those users. This paper uses stochastic geometry to model and analyze a spatially correlated heterogeneous vehicular network where both RSUs and vehicular relays serve network users such as pedestrians or other vehicles. We present an analytical model where the spatial correlation between roads, RSUs, relays, and users is systematically modeled via Cox point processes. Assuming users are associated with either RSUs or relays, we derive the association probability and the coverage probability of the typical user. Then, we derive the user throughput by considering interactions of links unique to the proposed network. This paper gives practical insights into designing spatially correlated vehicular networks assisted by vehicle relays. For instance, we express the network performance such as the user association, SIR coverage probability, and the network throughput as the functions of network key geometric variables. In practice, this helps one to optimize the network so as to achieve ultra reliability or maximum user throughput of a spatially correlated vehicular networks by varying key aspects such as the relay density or the bandwidth for relays.

翻译：在车辆对一切（V2X）通信中，路侧单元（RSU）在连接各类网络设备中起关键作用。但在某些场景下，由于拥塞、衰减或干扰，用户可能无法获得RSU的良好服务。此时，与RSU关联的车载中继可用于服务这些用户。本文采用随机几何方法对空间相关的异构车载网络进行建模与分析，该网络中RSU与车载中继共同服务行人或其他车辆等网络用户。我们提出一种分析模型，通过Cox点过程系统地建模道路、RSU、中继与用户之间的空间相关性。假设用户与RSU或中继关联，推导了典型用户的关联概率与覆盖概率。进而通过考虑该网络特有链路间的相互作用，推导用户吞吐量。本文为设计车载中继辅助的空间相关车载网络提供了实用见解。例如，我们将用户关联、信干比覆盖概率及网络吞吐量等性能指标表达为网络关键几何变量的函数。实际应用中，这有助于通过调整中继密度或中继带宽等关键参数，优化网络以达成空间相关车载网络的超可靠通信或最大化用户吞吐量。