Modulation Classification for Non-orthogonal Multiple Access System using a Modified Residual-CNN
Ashok Parmar, Kamal M. Captain, Udit Satija, Ankit Chouhan · 2023
Non-orthogonal multiple access (NOMA) is a promising solution to the problem of spectrum deficiency. NOMA receivers require information about the modulation type of the co-scheduled user's signal to perform successive interference cancellation (SIC). Automatic modulation classification (AMC) is used to reduce the signal overhead created due to sharing of this information. In this paper, a convolutional neural network (CNN) with a modified residual block (MR-CNN) is proposed for AMC in NOMA systems. The classification performance of MR-CNN is evaluated on the input signal with four different modulation formats at varying signal to noise ratios (SNRs). Experimental results demonstrate the proposed model can achieve more than 90% classification accuracy for SNRs higher than 10dB. Further, comparative analysis depicts that the proposed model outperforms existing methods in terms of classification accuracy at low SNRs and has similar performance at high SNRs.