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On routing in random Rayleigh fading networks
Abstract
This paper addresses the routing problem for large wireless networks of randomly distributed nodes with Rayleigh fading channels. First, we establish that the distances between neighboring nodes in a Poisson point process follow a generalized Rayleigh distribution. Based on this result, it is then shown that, given an end-to-end packet delivery probability (as a quality of service requirement), the energy benefits of routing over many short hops are significantly smaller than for deterministic network models that are based on the geometric disk abstraction. If the permissible delay for short-hop routing and long-hop routing is the same, it turns out that routing over fewer but longer hops may even outperform nearest-neighbor routing, in particular for high end-to-end delivery probabilities.
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Citations
Cited by
Stochastic geometry and random graphs for the analysis and design of wireless networks
Haenggi, M., Andrews, J. G., Baccelli, F., Dousse, O., & Franceschetti, M. (2009) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2009.090902 · Cited by 1,719
Self-citationDistance Distributions in Finite Uniformly Random Networks: Theory and Applications
Srinivasa, S., & Haenggi, M. (2010) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2009.2035044 · Cited by 285
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Metadata
- Authors
- Haenggi, M.
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2005-07
- Volume
- 4
- Issue
- 4
- Pages
- 1553-1562
- ISSN
- 1536-1276 (ISSN-L)
- OpenAlex
- W2149622712
- Cited by
- 177
- References
- 0
- FWCI
- 18.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- Haenggi, M. (2005). On routing in random Rayleigh fading networks. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/twc.2005.850376
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
