2021
DOI: 10.1177/15485129211049782
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Impact of the learning rate and batch size on NOMA system using LSTM-based deep neural network

Abstract: In this work, the deep learning (DL)-based fifth-generation (5G) non-orthogonal multiple access (NOMA) detector is investigated over the independent and identically distributed (i.i.d.) Nakagami- m fading channel conditions. The end-to-end system performance comparisons are given between the DL NOMA detector with the existing conventional successive interference cancelation (SIC)-based NOMA detector and from results, it has been proved that the DL NOMA detector performance is better than the convention SIC NOM… Show more

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Cited by 15 publications
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