TY - JOUR
T1 - AI-Driven FBMC-OQAM Signal Recognition via Transform Channel Convolution Strategy
AU - An, Zeliang
AU - ZHANG, Tianqi
AU - Liu, Debang
AU - Xu, Yuqing
AU - Pedersen, Gert Frølund
AU - Shen, Ming
PY - 2023/10/8
Y1 - 2023/10/8
N2 - With the advent of the Industry 5.0 era, the Internet of Things (IoT) devices face unprecedented proliferation, requiring higher communications rates and lower transmission delays. Considering its high spectrum efficiency, the promising filter bank multicarrier (FBMC) technique using offset quadrature amplitude modulation (OQAM) has been applied to Beyond 5G (B5G) industry IoT networks. However, due to the broadcasting nature of wireless channels, the FBMC-OQAM industry IoT network is inevitably vulnerable to adversary attacks from malicious IoT nodes. The FBMC-OQAM industry cognitive radio network (ICRNet) is proposed to ensure security at the physical layer to tackle the above challenge. As a pivotal step of ICRNet, blind modulation recognition (BMR) can detect and recognize the modulation type of malicious signals. The previous works need to accomplish the BMR task of FBMC-OQAM signals in ICRNet nodes. A novel FBMC BMR algorithm is proposed with the transform channel convolution network (TCCNet) rather than a complicated two-dimensional convolution. Firstly, this is achieved by designing a low-complexity binary constellation diagram (BCD) gridding matrix as the input of TCCNet. Then, a transform channel convolution strategy is developed to convert the image-like BCD matrix into a series-like data format, accelerating the BMR process while keeping discriminative features. Monte Carlo experimental results demonstrate that the proposed TCCNet obtains a performance gain of 8% and 40% over the traditional in-phase/quadrature (I/Q)-based and constellation diagram (CD)-based methods at a signal noise ratio (SNR) of 12 dB, respectively. Moreover, the proposed TCCNet can achieve around 29.682 and 2.356 times faster than existing CD-Alex Network (CD-AlexNet) and I/Q-Convolutional Long Deep Neural Network (I/Q-CLDNN) algorithms, respectively.
AB - With the advent of the Industry 5.0 era, the Internet of Things (IoT) devices face unprecedented proliferation, requiring higher communications rates and lower transmission delays. Considering its high spectrum efficiency, the promising filter bank multicarrier (FBMC) technique using offset quadrature amplitude modulation (OQAM) has been applied to Beyond 5G (B5G) industry IoT networks. However, due to the broadcasting nature of wireless channels, the FBMC-OQAM industry IoT network is inevitably vulnerable to adversary attacks from malicious IoT nodes. The FBMC-OQAM industry cognitive radio network (ICRNet) is proposed to ensure security at the physical layer to tackle the above challenge. As a pivotal step of ICRNet, blind modulation recognition (BMR) can detect and recognize the modulation type of malicious signals. The previous works need to accomplish the BMR task of FBMC-OQAM signals in ICRNet nodes. A novel FBMC BMR algorithm is proposed with the transform channel convolution network (TCCNet) rather than a complicated two-dimensional convolution. Firstly, this is achieved by designing a low-complexity binary constellation diagram (BCD) gridding matrix as the input of TCCNet. Then, a transform channel convolution strategy is developed to convert the image-like BCD matrix into a series-like data format, accelerating the BMR process while keeping discriminative features. Monte Carlo experimental results demonstrate that the proposed TCCNet obtains a performance gain of 8% and 40% over the traditional in-phase/quadrature (I/Q)-based and constellation diagram (CD)-based methods at a signal noise ratio (SNR) of 12 dB, respectively. Moreover, the proposed TCCNet can achieve around 29.682 and 2.356 times faster than existing CD-Alex Network (CD-AlexNet) and I/Q-Convolutional Long Deep Neural Network (I/Q-CLDNN) algorithms, respectively.
KW - FBMC, Modulation recognition, cognitive communications, deep learning
KW - Intelligent signal recognition
KW - transform channel convolution
KW - binary constellation diagram
KW - industrial cognitive radio networks
KW - FBMC-OQAM
UR - http://www.scopus.com/inward/record.url?scp=85174489839&partnerID=8YFLogxK
U2 - 10.32604/cmc.2023.037832
DO - 10.32604/cmc.2023.037832
M3 - Journal article
SN - 1546-2218
VL - 76
SP - 2817
EP - 2834
JO - Computers, Materials & Continua
JF - Computers, Materials & Continua
IS - 3
ER -