Predicting the performance of radio over free space optics system using machine learning techniques

Kaur, Sanmukh; Kaur, Jasleen; Sharma, Aanchal

doi:10.1016/j.ijleo.2023.170798

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…The variability of refractive index may be defined in terms of C n 2 [27]. The Rytov parameter s , 1 2 describes the turbulence effect on the radiated beam in terms of C n 2 by the following formula:…”

Section: System Modelmentioning

confidence: 99%

“…Strong (s 1 2 > 1), moderate (s 1 2 ∼ 1) and weak turbulence (s 1 2 < 1) are the three turbulence regimes that are typically categorized by s . 1 2 Modelling of the turbulence takes into account the probability density functions (PDFs) of the G-G and log-normal (LN) distributions [28]. The light intensity 'I' follows the LN distribution in a weak atmospheric condition, and its PDF is represented by:…”

Section: System Modelmentioning

confidence: 99%

“…In comparison to conventional communication systems, free space optics (FSO) links have many advantages, including low cost, quick and simple deployment, license-free spectrum availability, immunity to electromagnetic interference, lightweight equipment, high channel capacity, and secure data transmission [1]. A viable approach to meet the demand for high channel bandwidth in future wireless networks is the inclusion of radio-over-FSO (Ro-FSO) links [2]. The goal of Ro-FSO is to create a universal platform for heterogeneous wireless services that are affordable same as radio over fiber (RoF).…”

Section: Introductionmentioning

confidence: 99%

“…Comparative analysis of different ML algorithms in terms of R2 and RMSE.In this work, a terrestrial 64 QAM OFDM Ro-FSO system has been examined in terms of BER considering strong and weak air turbulence conditions employing log normal and gamma-gamma scintillation models. ANN, KNN, and DT models have been trained for performance estimation in terms of RMSE & R-squared values.…”

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confidence: 99%

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Machine learning based performance estimation of terrestrial Ro-FSO Link

Kaur

Singh²,

Sharma

2023

Phys. Scr.

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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.

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Machine learning based performance estimation of terrestrial Ro-FSO Link

Kaur

Singh²,

Sharma

2023

Phys. Scr.

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“…It is possible to achieve BERs of 10 −26 or lower using current optical communication technologies. As the technology continues to improve, it is likely that even lower BERs will be possible 16–18 …”

Section: Introductionmentioning

confidence: 99%

Simulation of all‐optical NOR gate design with improved extinction ratio using semiconductor optical amplifier

Vadukanathan,

Suyambragasam,

Sivaraj

et al. 2024

Micro & Optical Tech Letters

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Optical computers, leveraging the advantages of light characteristics, hold the potential to outperform electronic computers significantly. The role of all‐optical logic gates is pivotal in realizing such high‐performance computing systems. This research introduces a streamlined design for an all‐optical NOR logic gate utilizing a semiconductor optical amplifier. The proposed gate operates at a remarkable data rate of 40 Gbps, achieving an excellent extinction ratio of 20.285 dB. Notably, the design exhibits very low dispersion (−0.029 × 10+006 ps/nm) and an impressively low bit error rate of 4.27 × 10−26.

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Performance estimation of SISO and MIMO Ro-FSO link under atmospheric turbulence

Kaur,

Sharma

2023

Opt Quant Electron

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Predicting the performance of radio over free space optics system using machine learning techniques

Cited by 9 publications

References 32 publications

Machine learning based performance estimation of terrestrial Ro-FSO Link

Machine learning based performance estimation of terrestrial Ro-FSO Link

Simulation of all‐optical NOR gate design with improved extinction ratio using semiconductor optical amplifier

Performance estimation of SISO and MIMO Ro-FSO link under atmospheric turbulence

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