Tytuł:

Analysis of an LSTM-based NOMA Detector Over Time Selective Nakagami-m Fading Channel Conditions, Journal of Telecommunications and Information Technology, 2022, nr 3

Autor:

Shankar, Ravi ; Mehraj, Haider ; Bangare, Jyoti L. ; Kumar, Ajay ; Kulkarni, Shriram S. ; Gupta, Sandeep

Temat i słowa kluczowe:

non orthogonal multiple access (NOMA) ; orthogonal multiple access (OMA) ; deep learning (DL) ; multiple-input multiple-output (MIMO)

Opis:

This work examines the efficacy of deep learning (DL) based non-orthogonal multiple access (NOMA) receivers in vehicular communications (VC). Analytical formulations for the outage probability (OP), symbol error rate (SER), and ergodic sum rate for the researched vehicle networks are established Rusing i.i.d. Nakagami-m fading links. Standard receivers, such as least square (LS) and minimum mean square error (MMSE), are outperformed by the stacked long-short term memory (S-LSTM) based DL-NOMA receiver. Under real time propagation circumstances, including the cyclic prefix (CP) and clipping distortion, the simulation curves compare the performance of MMSE and LS receivers with that of the DL-NOMA receiver. According to numerical statistics, NOMA outperforms conventional orthogonal multiple access (OMA) by roughly 20% and has a high sum rate when considering i.i.d. fading links.

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Data wydania:

2022, nr 3

Typ zasobu:

artykuł

Format:

application/pdf

Identyfikator zasobu:

ISSN 1509-4553, on-line: ISSN 1899-8852

DOI:

10.26636/jtit.2022.161222

eISSN:

1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności