Study Develops Novel Technique for the Demodulation of OAM in Optical Underwater Communication

In an article published in the Journal of Marine Science and Engineering, researchers used the topological charge value of the orbital angular momentum (OAM) for classification labels' identification. They analyzed the ocean turbulence caused by different temperatures and salinity.

Study: Identification of Orbital Angular Momentum by Support Vector Machine in Ocean Turbulence. Image Credit: Feelindesign/Shutterstock.com

Basic information modulation dimensional resources of light waves (time, polarization, wavelength, frequency, amplitude, etc.) have been established and used in classic wireless optical communication. The researchers have paid close attention to the novel orbital angular momentum-based spatial information modulation (OAM).

Laguerre-Gaussian (LG) Beam

The Laguerre-Gaussian (LG) beam is an example of the most typical underwater channel OAM beam. Each photon's OAM mode value may be any integer. Several OAM modes are orthogonal to one another, suggesting that several OAM modes do not interfere during transmission, allowing OAM light to be used for wireless optical communication's coding, decoding, and multiplexing transmission to fulfill the need for information transmission capacity that is ever-growing.

However, OAM experiences rich information overload, mostly brought on by the OAM beam's anti-interference capability and transmission interference. Therefore, identifying the OAM is crucial because, in demanding circumstances such as in a complex and variable underwater environment, the beams interact with the OAM throughout the transmission process, making it more complicated for the receiver to identify the OAM.

The Method Used in this Study

The standard method for determining OAM is to count how many bright fringes appear in the intensity pattern after the vortex beam has passed through the grating. However, turbulence's effect necessitates a transceiver to be strongly aligned, which often leads to poor OAM identification. Using neural networks in conjunction with OAM identification has shown to be very accurate and promising.

This study develops a technique to determine the OAM mode by analyzing the histogram of oriented gradients (HOG) based on the support vector machine (SVM) model. The findings demonstrate the value of getting the HOG feature from the spatial phase map for OAM modal identification.

SVM and HOG Features

SVM is a machine model that uses statistical theory to categorize data. The fundamental tenet of SVM is finding the point that can connect all points nearest to the hyperplane with the greatest interval.

The local, regional gradient of the picture is calculated and counted to create HOG features. The primary goal of the HOG method is to determine the image's gradient to retrieve that information as a feature.

Ocean Turbulence Random Phase Screen Model

The power spectrum inversion approach was used to create an ocean turbulence model. MATLAB helped carry out an experimental simulation of OAM identification under the ocean turbulence channel. This model was used to evaluate the impact of ocean turbulence on beam transmission.

By creating a random phase screen model of ocean turbulence, the spatial phase map of the OAM mode is made, and the phase map is then used to extract HOG features. The experimentation's data volume is 500, 450, 400, 350, 300, and 250 pieces of 1~10, 1~9, 1~8, 1~7, 1~6 and 1~5 OAM modalities (L), respectively.

How Simulations Were Performed

Five simulations were run, and the average result was obtained to make the data accurate and credible. A training set and test set were created using the sample data collected in a 7:3 ratio. The researchers trained the model under transmission distance based on the impact of various beam waist radius (w) on the OAM modal identification rate. The OAM mode recognition rate is attained when the turbulence intensity is equivalent to the strong turbulence Cn2=10-13K2m-2/3.

Findings of the Study

The rate of OAM mode identification of SVM was investigated by modeling the transmission of LG beams via ocean turbulence. The degree of damage to the spatial phase diagram of the OAM mode was variable under various beam waist radii when turbulence is Cn2=10-13K2m-2/3. On the other hand, the parameters of the OAM beams' spatial dispersion were scarcely affected by ocean turbulence when the beam waist radius was equal to -2.0.

SVM can achieve a classification and recognition rate of 98.93% with L = 1 to 5, 98.89% with L = 1 to 6, 97.33% with L = 1 to 7, 96.66% with L = 1 to 8 to 9, 95.4% with L = 1 to 9, and 95.33% with L = 1 to 10.

Conclusion

The OAM modal recognition of ocean turbulence based on SVM, as well as the OAM modal recognition simulation under the ocean turbulence channel, were conducted in this study.

In addition, an analysis was carried out on how severe turbulence affected OAM's modal identification. The findings indicate that the OAM modal recognition's accuracy steadily rose when the beam waist radius was reduced. However, the identification accuracy rapidly declined as OAM modes increased. The findings of the experiments can be used to generate fresh concepts for optical underwater communication demodulation and research, which has significant research and experimental application value.

Reference

Xiaoji Li, Jiemei Huang and Leiming Sun (2022) Identification of Orbital Angular Momentum by Support Vector Machine in Ocean Turbulence. Journal of Marine Science and Engineering. https://www.mdpi.com/2077-1312/10/9/1284

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Taha Khan

Written by

Taha Khan

Taha graduated from HITEC University Taxila with a Bachelors in Mechanical Engineering. During his studies, he worked on several research projects related to Mechanics of Materials, Machine Design, Heat and Mass Transfer, and Robotics. After graduating, Taha worked as a Research Executive for 2 years at an IT company (Immentia). He has also worked as a freelance content creator at Lancerhop. In the meantime, Taha did his NEBOSH IGC certification and expanded his career opportunities.  

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