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4篇 您的检索式:作者名="Debang"
    题名 作者 年代 出处 被引量
1Multiple-symbol differential detection for MPSK space-time block codes: decision metric and performance analysis 显示文摘Gao Chunjun Haimovich Alexander M Lao Debang 2006IEEE Trans Commun2006,54,8:1
2A Solar Radio Spectrometer at 5.2–7.6?GHz显示文摘Huirong Ji Qijun Fu Yuying Liu Congling Cheng Zhijun Chen Yihua Yan Leping Zheng Zongjun Ning Chengmin Tan Debang Lao Shuke Li Jiyong Gao Zhiqiang Wang Minhong Yu 2003Solar Physics2003,,2:1
3Value of human papillomavirus testing affer conization by loop electrosurgical excision for high-grade squamous intraepithelial lesions显示文摘HOUFFLIN DEBANGE V 2003Gynecologic Oncology2003,90,3:1
4AI-Driven FBMC-OQAM Signal Recognition via Transform Channel Convolution Strategy显示文摘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-OQAMindustry IoT network is inevitably vulnerable to adversary attacks frommalicious IoT nodes.The FBMC-OQAMindustry 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 serieslike 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 inphase/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.Zeliang An Tianqi Zhang Debang Liu Yuqing Xu Gert Frølund Pedersen Ming Shen 2023Computers, Materials & Continua2023,76,9:0
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