TY - GEN
T1 - Measurement-based Modulation Classification in Unlicensed Millimeter-Wave Bands
AU - Sümen, Gizem
AU - Görçin, Ali
AU - Qaraqe, Khalid A.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Automatic modulation classification (AMC) facilitates adaptive modulation schemes, leading to the minimization of pilot signals, thus affecting spectral efficiency and reducing the power consumption in wireless communications systems. Since high-frequency heterogeneous and adaptive networks are established as future projections, AMC will also play a critical role in the millimeter-wave (mmWave) band communications. This study proposes multi-channel convolutional long short-term deep neural network (MCLDNN) model for AMC in mmWave bands. The performance of the proposed method is evaluated under real conditions based on a measurement campaign. 802.11ad signals are utilized for the measurements in 57.24 GHz to 59.40 GHz band. The classification performance of the proposed model is compared with that of well-known deep-learning methods, i.e., convolutional neural network and convolutional long short-term deep neural network. The measurement results imply the robustness of the proposed method to real-life conditions and its superiority against contemporary networks, especially in low signal-to-noise ratio (SNR) region.
AB - Automatic modulation classification (AMC) facilitates adaptive modulation schemes, leading to the minimization of pilot signals, thus affecting spectral efficiency and reducing the power consumption in wireless communications systems. Since high-frequency heterogeneous and adaptive networks are established as future projections, AMC will also play a critical role in the millimeter-wave (mmWave) band communications. This study proposes multi-channel convolutional long short-term deep neural network (MCLDNN) model for AMC in mmWave bands. The performance of the proposed method is evaluated under real conditions based on a measurement campaign. 802.11ad signals are utilized for the measurements in 57.24 GHz to 59.40 GHz band. The classification performance of the proposed model is compared with that of well-known deep-learning methods, i.e., convolutional neural network and convolutional long short-term deep neural network. The measurement results imply the robustness of the proposed method to real-life conditions and its superiority against contemporary networks, especially in low signal-to-noise ratio (SNR) region.
KW - Automatic modulation classification
KW - Convolutional neural network
KW - Deep learning
KW - Unlicensed millimeter-wave
UR - http://www.scopus.com/inward/record.url?scp=85159781403&partnerID=8YFLogxK
U2 - 10.1109/WCNC55385.2023.10119008
DO - 10.1109/WCNC55385.2023.10119008
M3 - Conference contribution
AN - SCOPUS:85159781403
T3 - Ieee Wireless Communications And Networking Conference
BT - 2023 Ieee Wireless Communications And Networking Conference, Wcnc
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023
Y2 - 26 March 2023 through 29 March 2023
ER -