- Tytuł:
- Analysis of an LSTM-based NOMA Detector Over Time Selective Nakagami-m Fading Channel Conditions
- Autorzy:
-
Shankar, Ravi
Bangare, Jyoti L.
Kumar, Ajay
Gupta, Sandeep
Mehraj, Haider
Kulkarni, Shriram S. - Powiązania:
- https://bibliotekanauki.pl/articles/2142313.pdf
- Data publikacji:
- 2022
- Wydawca:
- Instytut Łączności - Państwowy Instytut Badawczy
- Tematy:
-
deep learning
DL
multiple-input multiple-output
MIMO
non-orthogonal multiple access
NOMA
orthogonal multiple access
OMA - 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.
- Źródło:
-
Journal of Telecommunications and Information Technology; 2022, 3; 17--24
1509-4553
1899-8852 - Pojawia się w:
- Journal of Telecommunications and Information Technology
- Dostawca treści:
- Biblioteka Nauki
Artykuł