Pandey, KaushlendraGupta, Abhishek K.Dhillon, Harpreet S.Perumalla, Kanaka Raju2024-02-012024-02-012023-12-04Pandey, K.; Gupta, A.K.; Dhillon, H.S.; Perumalla, K.R. Properties of a Random Bipartite Geometric Associator Graph Inspired by Vehicular Networks. Entropy 2023, 25, 1619.https://hdl.handle.net/10919/117812We consider a point process (PP) generated by superimposing an independent Poisson point process (PPP) on each line of a 2D Poisson line process (PLP). Termed PLP-PPP, this PP is suitable for modeling networks formed on an irregular collection of lines, such as vehicles on a network of roads and sensors deployed along trails in a forest. Inspired by vehicular networks in which vehicles connect with their nearest wireless base stations (BSs), we consider a <i>random bipartite associator graph</i> in which each point of the PLP-PPP is associated with the nearest point of an independent PPP through an edge. This graph is equivalent to the partitioning of PLP-PPP by a Poisson Voronoi tessellation (PVT) formed by an <i>independent</i> PPP. We first characterize the exact distribution of the number of points of PLP-PPP falling inside the ball centered at an arbitrary location in <inline-formula><math display="inline"><semantics><msup><mi mathvariant="double-struck">R</mi><mn>2</mn></msup></semantics></math></inline-formula> as well as the typical point of PLP-PPP. Using these distributions, we derive cumulative distribution functions (CDFs) and probability density functions (PDFs) of <i>k</i>th contact distance (CD) and the nearest neighbor distance (NND) of PLP-PPP. As intermediate results, we present the empirical distribution of the perimeter and approximate distribution of the length of the typical chord of the zero-cell of this PVT. Using these results, we present two close approximations of the distribution of node degree of the random bipartite associator graph. In a vehicular network setting, this result characterizes the number of vehicles connected to each BS, which models its <i>load</i>. Since each BS has to distribute its limited resources across all the vehicles connected to it, a good statistical understanding of load is important for an efficient system design. Several applications of these new results to different wireless network settings are also discussed.application/pdfenCreative Commons Attribution 4.0 InternationalPoisson line processPoisson point processCox processload distribution in vehicular communicationvehicular networkArticle - Refereed2023-12-22https://doi.org/10.3390/e25121619