Abstract
In this paper, we propose a new automatic modulation classification (AMC) scheme for aggregated physical downlink shared channel (PDSCH) in 5G New Radio (NR). First, we develop a generic convolutional neural network (CNN) structure for classifying modulation types specified in 5G NR which are QPSK, 16QAM, 64QAM, and 256 QAM. The received n aggregated PDSCHs are divided into multiple segments, then the received symbols within same group are softly combined together. After that, a set of combined symbols are input to the CNN-based classifier. Then, the classification results are further fused in three different ways to improve the classification accuracy. From the simulations, it is observed that the classification accuracy can be maximized by adjusting the soft-combining level for the received PDSCHs considering trade-off between signal-to-noise ratio gain and diversity gain.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
