Experimental Evaluation of Measurement-based SINR Interference Models

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Tan, Wee Lum; Hu, Peizhao; Portmann, Marius


Conference Material

IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

San Francisco, USA


In 802.11-based wireless networks, the ability to accurately predict the impact of interference via the use of an interference model is essential to better and more efficient channel assignment algorithms and data routing protocols. Recently, there have been several works that proposed new interference models utilizing the well-known concept of signal-to-interference-plus-noise ratio (SINR). Using active measurements, these models construct a profile that maps either the measured received signal strength (RSS) or the computed SNR or SINR values at a receiver, to its packet delivery ratio (PDR) performance. The profile is then used by the models to predict the PDR performance in more complex scenarios involving multiple interferers. While comparison with other basic models (e.g. hop-based and distance-based) have been made in these works, there has as yet been no comprehensive comparison on the accuracy of these measurement-based SINR interference models. In this paper, we systematically evaluate the performance of three measurement-based SINR interference models in predicting the interference impact on the successful reception of packets. Our evaluations cover various interference scenarios with both 802.11 and non-802.11 interferers, in experiments carried out in both our conducted testbed and an over-the-air testbed. Our results show that an interference model that utilizes an SINR profile can accurately predict the PDR performance with a maximum root-mean square error (RMSE) of 10.8% across all our evaluations. In contrast, interference models that rely on the SNR profile and the RSS profile perform poorly, with a maximum RMSE of 61.7% and 66.1% respectively.



Tan, Wee Lum; Hu, Peizhao; Portmann, Marius. Experimental Evaluation of Measurement-based SINR Interference Models.[Conference Material]. 2012-06-26. <a href="http://hdl.handle.net/102.100.100/100349?index=1" target="_blank">http://hdl.handle.net/102.100.100/100349?index=1</a>

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