In mathematics and probability theory, continuum percolation theory is a branch of mathematics that extends discrete percolation theory to continuous space (often Euclidean space ℝn). More specifically, the underlying points of discrete percolation form types of lattices whereas the underlying points of continuum percolation are often randomly positioned in some continuous space and form a type of point process. For each point, a random shape is frequently placed on it and the shapes overlap each with other to form clumps or components. As in discrete percolation, a common research focus of continuum percolation is studying the conditions of occurrence for infinite or giant components.[1][2] Other shared concepts and analysis techniques exist in these two types of percolation theory as well as the study of random graphs and random geometric graphs.
Continuum percolation arose from an early mathematical model for wireless networks,[2][3] which, with the rise of several wireless network technologies in recent years, has been generalized and studied in order to determine the theoretical bounds of information capacity and performance in wireless networks.[4][5] In addition to this setting, continuum percolation has gained application in other disciplines including biology, geology, and physics, such as the study of porous material and semiconductors, while becoming a subject of mathematical interest in its own right.[6]
^Meester, R. (1996). Continuum Percolation. Vol. 119. Cambridge University Press.[ISBN missing]
^ abFranceschetti, M.; Meester, R. (2007). Random Networks for Communication: From Statistical Physics to Information Systems. Vol. 24. Cambridge University Press.[ISBN missing]
^Gilbert, E. N. (1961). "Random plane networks". Journal of the Society for Industrial and Applied Mathematics. 9 (4): 533–543. doi:10.1137/0109045.
^Dousse, O.; Baccelli, F.; Thiran, P. (2005). "Impact of interferences on connectivity in ad hoc networks". IEEE/ACM Transactions on Networking. 13 (2): 425–436. CiteSeerX 10.1.1.5.3971. doi:10.1109/tnet.2005.845546. S2CID 1514941.
^Dousse, O.; Franceschetti, M.; Macris, N.; Meester, R.; Thiran, P. (2006). "Percolation in the signal to interference ratio graph". Journal of Applied Probability. 2006 (2): 552–562. doi:10.1239/jap/1152413741.
^Balberg, I. (1987). "Recent developments in continuum percolation". Philosophical Magazine B. 56 (6): 991–1003. Bibcode:1987PMagB..56..991B. doi:10.1080/13642818708215336.
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