Optically assisted mm-wave-based multi-Gbps RoFSO transmission link under channel fading models

Tripathi, Abhishek Kumar; Gupta, Shilpi; Mandloi, Abhilash; Soni, Gireesh G.

doi:10.1080/09500340.2022.2041754

Cited by 9 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The transmission range is extended by a factor of (2.9 to 1.7) km when the QAM-16 modulation is employed in composite (HG00 + HG01) mode and transmitted in the same weather conditions with the tolerable bit error rate (BER) of 10 −5 . In 2022 [21], by using optical links in the turbulent atmosphere, the authors investigated a RF carrying signal for dual hop FSO/RF links of 500 m/50 m. The author analyzed Gamma-Gamma (G-G) and Lognormal (LN) models and achieved a BER of 10 −6 . In 2022 [8], clear, haze, rain and fog conditions with attenuation losses of 0.2 dB km −1 , 2.3 dB km −1 , 4.3 dB km −1 and 8 dB km −1 have been considered for the performance enhancement in the link range of about 46.1%, 35%, 29.4% and 25.9%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based performance estimation of terrestrial Ro-FSO Link

Kaur

Singh²,

Sharma

2023

Phys. Scr.

View full text Add to dashboard Cite

In this work, a radio over free-space optical communication (Ro-FSO) link has been examined considering quadrature amplitude modulation (64-QAM) based orthogonal frequency division multiplexing (OFDM) technique for a turbulence channel. The performance of the system has been investigated considering log normal and gamma-gamma atmospheric scintillation models under clear air, rain and fog weather conditions. Artificial neural network (ANN), k-nearest neighbour (KNN), and decision tree (DT) machine learning (ML) techniques have been applied for estimation of quality of received signal in terms of bit error rate BER. ANN model exhibits the highest value of R-squared (R2) of 0.9967 and lowest value of root mean square error (RMSE) of 0.0134 as compared to other ML techniques resulting in the best fit model.

show abstract

Section: Introductionmentioning

confidence: 99%