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.
Inzerillo, N., Croce, D., Garlisi, D., Giuliano, F., Tinnirello, I. (2018). Error-Based Interference Detection in WiFi Networks. In 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. [10.1109/GLOCOM.2017.8254097].
Error-Based Interference Detection in WiFi Networks
Inzerillo, Nicola;Croce, Daniele;Garlisi, Domenico;Giuliano, Fabrizio;Tinnirello, Ilenia
2018-01-01
Abstract
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.File | Dimensione | Formato | |
---|---|---|---|
08254097.pdf
Solo gestori archvio
Tipologia:
Versione Editoriale
Dimensione
547.4 kB
Formato
Adobe PDF
|
547.4 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.