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Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI, Journal of Telecommunications and Information Technology, 2022, nr 4
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Tytuł: Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI, Journal of Telecommunications and Information Technology, 2022, nr 4

Autor:

Shankar, Ravi ; Mishra, Ritesh Kumar ; Chaudhary, Bhanu Pratap

Data wydania:

2022, nr 4

Typ zasobu:

artykuł

Opis:

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.

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Format:

application/pdf

Identyfikator zasobu:

oai:bc.itl.waw.pl:2253 ; ISSN 1509-4553, on-line: ISSN 1899-8852

DOI:

10.26636/jtit.2022.165722

eISSN:

ISSN 1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności

Temat i słowa kluczowe:

MIMO NOMA zero-mean circularly symmetric complex Gaussian (ZM-CSCG) S-LSTM orthogonal frequency division multiplexing (OFDM) inter-symbol interference

Kolekcje, do których przypisany jest obiekt:

Data ostatniej modyfikacji:

6 cze 2024

Data dodania obiektu:

7 lut 2023

Liczba wyświetleń treści obiektu:

36

Wszystkie dostępne wersje tego obiektu:

https://bc.itl.waw.pl/publication/2562

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Nazwa wydania Data
Chaudhary, Bhanu Pratap, 2022, nr 4, Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI, Journal of Telecommunications and Information Technology, 2022, nr 4 6 cze 2024

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