DNN-AIDED CODEBOOK DESIGN WITH MPA DECODING USINGDEEP NEURAL NETWORK

Abstract

parse Code Multiple Access (SCMA) is an extremely effective non-orthogonal multiple access tech-nology that enables communication between users who have limited orthogonal resources currently. Traditional SCMA methods use manually designed codebooks, potentially leading to subpar performance owing to inadequate optimization for certain encoders. A Deep Neural Network (DNN) is used to produce a deep learning for SCMA codebook using Stochastic Gradient Descent (SGD). This enables the model to determine the optimal weights and biases for generating precise predictions. The proposed approach surpasses existing techniques in a Rayleigh fading channel due to its reduced Bit Error Rate (BER), enhanced Minimum Euclidean Distance (MED), and diminished complexity compared to prior SCMA frameworks.