6533b854fe1ef96bd12adeef
RESEARCH PRODUCT
Error-Based Interference Detection in WiFi Networks
Domenico GarlisiDaniele CroceNicola InzerilloIlenia TinnirelloFabrizio Giulianosubject
Artificial Neural NetworkNeuronsMonitoringComputer scienceSettore ING-INF/03 - Telecomunicazioni05 social sciencesReal-time computingComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS050801 communication & media studies020206 networking & telecommunicationsWireless LAN02 engineering and technologySpectrum managementReceiversZigBee0508 media and communicationsComputer Networks and CommunicationPHYHardware and Architecture0202 electrical engineering electronic engineering information engineeringLong Term EvolutionDemodulationWireless fidelitySafety Risk Reliability and QualityInterferencedescription
In this paper we show that inter-technology interference can be recognized by commodity WiFi devices by monitoring the statistics of receiver errors. Indeed, while for WiFi standard frames the error probability varies during the frame reception in different frame fields (PHY, MAC headers, payloads) protected with heterogeneous coding, errors may appear randomly at any point during the time the demodulator is trying to receive an exogenous interfering signal. We thus detect and identify cross-technology interference on off-the-shelf WiFi cards by monitoring the sequence of receiver errors (bad PLCP, bad PCS, invalid headers, etc.) and develop an Artificial Neural Network (ANN) to recognize the source of interference. The result is quite impressive, reaching an average accuracy of almost 99% in recognizing ZigBee, Microwave and LTE (in unlicensed spectrum) interference.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-01 |