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Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI

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Języki publikacji
EN
Abstrakty
EN
In this paper, the performance of a deep learningbased 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. Furthermore, 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.
Rocznik
Tom
Strony
47--59
Opis fizyczny
Bibliogr. 53 poz., rys., wykr.
Twórcy
autor
  • Electronics and Communication Engineering Department, SR University, India
  • Electronics and Communication Engineering Department, National Institute of Technology Patna, India
  • Electronics and Communication Engineering Department, National Institute of Technology Patna, India
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Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4d164401-5c55-4f15-9581-7835bd8d1fc6
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