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Stochastic Modeling of Microwave Oven Interference in WLANs

Marcel Nassar and Brian Evans The University of Texas at Austin, Austin, Texas USA Email: mnassar@utexas.edu , bevans@ece.utexas.edu Xintian Eddie Lin Intel Corporation, Santa Clara, California USA Email: eddie.x.lin@intel.com. Microwaves and WLANs. Spectral and Temporal Properties.

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Stochastic Modeling of Microwave Oven Interference in WLANs

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  1. Marcel Nassarand Brian EvansThe University of Texas at Austin, Austin, Texas USAEmail: mnassar@utexas.edu, bevans@ece.utexas.edu Xintian Eddie LinIntel Corporation, Santa Clara, California USAEmail: eddie.x.lin@intel.com Microwaves and WLANs Spectral and Temporal Properties • Microwave oven interference model will have applications in: • System Simulations • Insight into PHY Layer receiver design • Tuning of MAC Layer parameters for optimized performance for delay sensitive applications • Microwave Ovens operating in the 2.4GHz unlicensed ISM band interfere with IEEE802.11b/g/n WLANS • Microwave Interference leads disruptions of transmission or dramatic increase in bit error rates • Leads to huge degradation in performance for delay sensitive applications such as streaming • The on-time of the oven has also some time where the interference is low • This is due to frequency drift phenomena Stochastic Modeling of Microwave Oven Interference in WLANs • The spectrogram shows the frequency drift phenomena • Each WLAN channel observes different temporal noise properties • The temporal traces exhibits variations as a function of frequency as well • Max-hold power spectral density of microwave oven interference indicates that it spans all WLAN bands Communication Performance Microwave Oven Interference Modeling • Accurate modeling leads increase in available information rate • The decrease in available capacity is significantly less than predicted by other models • The accurate modeling leads to insights into receiver design and optimization of its parameters • The proposed model captures the frequency dependence of the noise trace. Thus enabling channel-level simulations. • The instantaneous statistics of the proposed model and real interference data shows that our model’s prediction provides the best fit. • The available rate increases with distance between the receiver and the oven • At lower distances, avoidance achieves the best available rate due to the high interference caused by the oven ON-time • At higher distances, transmitting during the ON-time of the oven provides increase in rate because the pathloss attenuates the oven interference This research was supported by Intel Corporation

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