Properties of a Random Bipartite Geometric Associator Graph Inspired by Vehicular Networks
dc.contributor.author | Pandey, Kaushlendra | en |
dc.contributor.author | Gupta, Abhishek K. | en |
dc.contributor.author | Dhillon, Harpreet S. | en |
dc.contributor.author | Perumalla, Kanaka Raju | en |
dc.date.accessioned | 2024-02-01T14:34:24Z | en |
dc.date.available | 2024-02-01T14:34:24Z | en |
dc.date.issued | 2023-12-04 | en |
dc.date.updated | 2023-12-22T13:45:04Z | en |
dc.description.abstract | We 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. | en |
dc.description.version | Published version | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.citation | Pandey, 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. | en |
dc.identifier.doi | https://doi.org/10.3390/e25121619 | en |
dc.identifier.uri | https://hdl.handle.net/10919/117812 | en |
dc.language.iso | en | en |
dc.publisher | MDPI | en |
dc.rights | Creative Commons Attribution 4.0 International | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
dc.subject | Poisson line process | en |
dc.subject | Poisson point process | en |
dc.subject | Cox process | en |
dc.subject | load distribution in vehicular communication | en |
dc.subject | vehicular network | en |
dc.title | Properties of a Random Bipartite Geometric Associator Graph Inspired by Vehicular Networks | en |
dc.title.serial | Entropy | en |
dc.type | Article - Refereed | en |
dc.type.dcmitype | Text | en |