Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-10T14:10:02.140Z Has data issue: false hasContentIssue false

Moments of k-hop counts in the random-connection model

Published online by Cambridge University Press:  11 December 2019

Nicolas Privault*
Affiliation:
Nanyang Technological University

Abstract

We derive moment identities for the stochastic integrals of multiparameter processes in a random-connection model based on a point process admitting a Papangelou intensity. The identities are written using sums over partitions, and they reduce to sums over non-flat partition diagrams if the multiparameter processes vanish on diagonals. As an application, we obtain general identities for the moments of k-hop counts in the random-connection model, which simplify the derivations available in the literature.

MSC classification

Type
Research Papers
Copyright
© Applied Probability Trust 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bassan, B. and Bona, E. (1990). Moments of stochastic processes governed by Poisson random measures. Comment. Math. Univ. Carolin. 31, 337343.Google Scholar
Bogdan, K., Rosiński, J., Serafin, G. and Wojciechowski, L. (2017). Lévy systems and moment formulas for mixed Poisson integrals. In Stochastic Analysis and Related Topics (Progr. Probab. Vol. 72), Birkhäuser/Springer, Cham, pp. 139164.CrossRefGoogle Scholar
Boyadzhiev, K. (2009). Exponential polynomials, Stirling numbers, and evaluation of some gamma integrals. Abstr. Appl. Anal. 2009, Art. ID 168672.CrossRefGoogle Scholar
Breton, J.-C. and Privault, N. (2014). Factorial moments of point processes. Stoch. Process. Appl. 124, 34123428.CrossRefGoogle Scholar
Decreusefond, L. and Flint, I. (2014). Moment formulae for general point processes. J. Funct. Anal. 267, 452476.CrossRefGoogle Scholar
Decreusefond, L., Flint, I., Privault, N. and Torrisi, G. (2016). Determinantal point processes. In Stochastic Analysis for Poisson Point Processes: Malliavin Calculus, Wiener–Itô Chaos Expansions and Stochastic Geometry, eds G. Peccati and M. Reitzner. Springer, Berlin, pp. 311342.CrossRefGoogle Scholar
Deng, N., Zhou, W. and Haenggi, M. (2015). The Ginibre point process as a model for wireless networks with repulsion. IEEE Trans. Commun, Wireless . 14, 107121.CrossRefGoogle Scholar
Kallenberg, O. (1986). Random Measures, 4th edn. Akademie, Berlin.Google Scholar
Kartun-Giles, A. and Kim, S. (2018). Counting k-hop paths in the random connection model. IEEE Trans. Commun, Wireless . 17, 32013210.CrossRefGoogle Scholar
Kong, H. B., Flint, I., Wang, P., Niyato, D. and Privault, N. (2016). Exact performance analysis of ambient RF energy harvesting wireless sensor networks with Ginibre point process. IEEE J. Sel. Areas Commun. 34, 37693784.CrossRefGoogle Scholar
Mecke, J. (1967). Stationäre zufällige Masse auf lokalkompakten Abelschen Gruppen. Z. Wahrscheinlichkeitsth. 9, 3658.CrossRefGoogle Scholar
Meester, R. and Roy, R. (1996). Continuum Percolation (Camb. Tracts Math. Vol. 119). Cambridge University Press.Google Scholar
Miyoshi, N. and Shirai, T. (2012) A cellular network model with Ginibre configurated base stations. Research Report on Mathematical and Computational Sciences (Tokyo Inst. Tech.).Google Scholar
Nguyen, X. and Zessin, H. (1979). Integral and differential characterization of the Gibbs process. Math. Nachr. 88, 105115.Google Scholar
Peccati, G. and Taqqu, M. (2011). Wiener Chaos: Moments, Cumulants and Diagrams: A survey with Computer Implementation. Springer, New York.CrossRefGoogle Scholar
Privault, N. (2009). Moment identities for Poisson–Skorohod integrals and application to measure invariance. C. R. Math. Acad. Sci. Paris 347, 10711074.CrossRefGoogle Scholar
Privault, N. (2012). Invariance of Poisson measures under random transformations. Ann. Inst. H. Poincaré Probab. Statist. 48, 947972.CrossRefGoogle Scholar
Privault, N. (2012). Moments of Poisson stochastic integrals with random integrands. Prob. Math. Statist. 32, 227239.Google Scholar
Privault, N. (2016). Combinatorics of Poisson stochastic integrals with random integrands. In Stochastic Analysis for Poisson Point Processes: Malliavin Calculus, Wiener–Itô Chaos Expansions and Stochastic Geometry, eds G. Peccati and M. Reitzner. Springer, Berlin, pp. 3780.CrossRefGoogle Scholar
Slivnyak, I. (1962). Some properties of stationary flows of homogeneous random events. Theory Prob. Appl. 7, 336341.CrossRefGoogle Scholar