TY - CONF
T1 - Error-Based Interference Detection in WiFi Networks
AU - Croce, Daniele
AU - Tinnirello, Ilenia
AU - Garlisi, Domenico
AU - Croce, Daniele
AU - Garlisi, Domenico
AU - Giuliano, Fabrizio
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Artificial Neural Networks; Interference; Wireless LAN; Computer Networks and Communications; Hardware and Architecture; Safety
KW - Reliability and Quality
KW - Risk
KW - Artificial Neural Networks; Interference; Wireless LAN; Computer Networks and Communications; Hardware and Architecture; Safety
KW - Reliability and Quality
KW - Risk
UR - http://hdl.handle.net/10447/344421
M3 - Other
SP - 1
EP - 6
ER -