Geometric modeling" idealizes the spatial geometric relationships among the transmitter, the scatterers, and the receiver in a wireless propagation channel-to produce closed-form formulas of various channel-fading metrics (e.g., the distribution of the azimuth angle-of-arrival of the arriving multipaths). Scattered in the open literature are numerous such "geometric models," each advancing its own closed-form formula of a fading metric, each based on a different idealization of the spatial geometry of the scatterers. Lacking in the open literature is a comprehensive and critical comparison among all such single-cluster geometric-model-based formulas of the arriving multipaths' azimuth direction-of-arrival distribution. This paper fills this literature gap. The comparison here uses all empirical data legibly available in the open literature for landmobile wireless radiowave propagation. No one geometric model is best by all criteria and for all environments. However, a safe choice is the model with a Gaussian density of scatterers centered at the transmitter. Despite this model's simplicity of having only one degree of freedom, it is always either the best fitting model or offers an LSE within one third of an order-of-magnitude as the best fitting model for all empirical dataset of all environments.

This letter presents the joint/marginal distributions of the uplink (downlink) multipaths' azimuth angles of arrival and times of arrival at the cellular base station (mobile). These closedform explicit densities are rigorously derived based on a new "geometrical model" wherein omnidirectional scatterers are modeled as spatially distributed at an inverted parabolic spatial distribution on a two-dimensional disc centered at the mobile. This "inverted parabolic" density, in contrast to the uniform disc density or the rim density, indirectly accounts for scattering power loss. This new model better least-squares-fit some empirical data than its competing models can.

Intersections are critical areas of the transportation infrastructure associated with 47% of all road accidents. Vehicleto-vehicle (V2V) communication has the potential of preventing up to 35% of such serious road collisions. In fact, under the 5G/LTE Rel.15+ standardization, V2V is a critical use-case not only for the purpose of enhancing road safety, but also for enabling traffic efficiency in modern smart cities. Under this anticipated 5G definition, high reliability of 0.99999 is expected for semi-autonomous vehicles (i.e., driver-in-the-loop). As a consequence, there is a need to assess the reliability, especially for accident-prone areas, such as intersections. We unpack traditional average V2V reliability in order to quantify its related fine-grained V2V reliability. Contrary to existing work on infinitely large roads, when we consider finite road segments of significance to practical real-world deployment, fine-grained reliability exhibits bimodal behavior. Performance for a certain vehicular traffic scenario is either very reliable or extremely unreliable, but nowhere in relative proximity to the average performance.

Abstract-Vehicle-to-vehicle (V2V) communication can improve road safety and traffic efficiency, particularly around critical areas such as intersections. We analytically derive V2V success probability near an urban intersection, based on empirically supported line-of-sight (LOS), weak-line-of-sight (WLOS), and nonline-of-sight (NLOS) channel models. The analysis can serve as a preliminary design tool for performance assessment over different system parameters and target performance requirements.

Abstract-While analyzing mobile systems we often approximate the actual coverage surface and assume an ideal cell shape. In a multi-cellular network, because of its tessellating nature, a hexagon is more preferred than a circular geometry. Despite this reality, perhaps due to the inherent simplicity, only a model for circular based random spreading is available. However, if used, this results an unfair terminal distribution for non-circular contours. Therefore, in this paper we specifically derived an unbiased node density model for a hexagon. We then extended the principle and established stochastic ways to handle sectored cells. Next, based on these mathematical findings, we created a generic modeling tool that can support a complex network with varying position, capacity, size, user density, and sectoring capability. Last, simulation was used to verify the theoretical analysis.