Search for: [Description = "In this paper, the performance of a deep learning based multiple\-input multiple\-output \(MIMO\) non\-orthogonal multiple access \(NOMA\) system is investigated for 5G radio communication networks. We consider independent and identically distributed \(i.i.d.\) Nakagami\-m fading links to prove that when using MIMO with the NOMA system, the outage probability \(OP\) and end\-to\-end symbol error rate \(SER\) improve, even in the presence of imperfect channel state information \(CSI\) and successive interference cancellation \(SIC\) errors. Further more, the stacked long short\-term memory \(S\-LSTM\) algorithm is employed to improve the system’s performance, even under time\-selective channel conditions and in the presence of terminal’s mobility. For vehicular NOMA networks, OP, SER, and ergodic sum rate have been formulated. Simulations show that an S\-LSTM\-based DL\-NOMA receiver outperforms least square \(LS\) and minimum mean square error \(MMSE\) receivers. Furthermore, it has been discovered that the performance of the end\-to\-end system degrades with the growing amount of node mobility, or if CSI knowledge remains poor. Simulated curves are in close agreement with the analytical results."]

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