TY - GEN
T1 - Deep Learning for Interference Identification
T2 - 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
AU - Zhang, Xiwen
AU - Seyfi, Tolunay
AU - Ju, Shengtai
AU - Ramjee, Sharan
AU - Gamal, Aly El
AU - Eldar, Yonina C.
N1 - Publisher Copyright: © 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - 1We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi. We employ deep learning algorithms trained on received samples taken from a 10 MHz band in the 2.4 GHz ISM Band. We obtain a classification accuracy of around 89.5% using any of four different deep neural network architectures: CNN, ResNet, CLDNN, and LSTM, which demonstrate the generality of the effectiveness of deep learning at the considered task. Interestingly, our proposed CNN architecture requires approximately 60% of the training time required by the state of the art while achieving slightly larger classification accuracy. We then focus on the CNN architecture and further optimize its training time while incurring minimal loss in classification accuracy using three different approaches: 1-Band Selection, where we only use samples belonging to the lower and uppermost 2 MHz bands, 2-SNR Selection, where we only use training samples belonging to a single SNR value, and 3-Sample Selection, where we try various sub-Nyquist sampling methods to select the subset of samples most relevant to the classification task. Our results confirm the feasibility of fast deep learning for wireless interference identification, by showing that the training time can be reduced by as much as 30x with minimal loss in accuracy.
AB - 1We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi. We employ deep learning algorithms trained on received samples taken from a 10 MHz band in the 2.4 GHz ISM Band. We obtain a classification accuracy of around 89.5% using any of four different deep neural network architectures: CNN, ResNet, CLDNN, and LSTM, which demonstrate the generality of the effectiveness of deep learning at the considered task. Interestingly, our proposed CNN architecture requires approximately 60% of the training time required by the state of the art while achieving slightly larger classification accuracy. We then focus on the CNN architecture and further optimize its training time while incurring minimal loss in classification accuracy using three different approaches: 1-Band Selection, where we only use samples belonging to the lower and uppermost 2 MHz bands, 2-SNR Selection, where we only use training samples belonging to a single SNR value, and 3-Sample Selection, where we try various sub-Nyquist sampling methods to select the subset of samples most relevant to the classification task. Our results confirm the feasibility of fast deep learning for wireless interference identification, by showing that the training time can be reduced by as much as 30x with minimal loss in accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85072329385&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/SPAWC.2019.8815481
DO - https://doi.org/10.1109/SPAWC.2019.8815481
M3 - منشور من مؤتمر
SN - 9781538665299
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
SP - 1
EP - 5
BT - 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
Y2 - 2 July 2019 through 5 July 2019
ER -