Sniffing Out the Correct Physical Layer Capture Model in 802.11b
ICNP '04 Proceedings of the 12th IEEE International Conference on Network Protocols
Measurement-based models of delivery and interference in static wireless networks
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
A general model of wireless interference
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
A measurement-based approach to modeling link capacity in 802.11-based wireless networks
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Revamping the IEEE 802.11a PHY simulation models
Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems
What is the right model for wireless channel interference?
IEEE Transactions on Wireless Communications
Forty data communications research questions
ACM SIGCOMM Computer Communication Review
A site-specific indoor link model for realistic wireless network simulations
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
An empirical interference modeling for link reliability assessment in wireless networks
IEEE/ACM Transactions on Networking (TON)
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A typical wireless network exhibits time-varying channel conditions and complex interference relationships, which are also influenced by different vendor implementations. Therefore, to improve the mapping between wireless simulation and real life, site and device specific models are needed. In this paper, we propose such interference models, called BOWLsim-advanced, based on measurements in two WiFi testbeds. We validate BOWLsim-advanced in complex interference scenarios comprising of hidden terminals, multi-hop flows, and inter-flow interference and show that it has better accuracy compared to both our earlier work and the default interference model of ns-3 (Network Simulator 3). However, the maximum improvement is observed for hidden terminals at the PHY layer, and for multi-hop flows and unicast communication, performance varies based on whether nodes are able to carrier-sense each other or have asymmetric interference. Essentially, our results show that PHY-layer models become insufficient for asymmetric interference, which requires better MAC layer models for higher accuracy.