Identification of Orbital Angular Momentum by Support Vector Machine in Ocean Turbulence

Abstract

With the advancement of underwater communication technology, the traditional modulation dimension has been introduced, developed and utilized. In addition, orbital angular momentum (OAM) is utilized as the modulation dimension for optical underwater communication to obtain larger spectrum resources. The OAM features are extracted using a histogram of oriented gradient and trained using the support vector machine method with a gradient direction histogram feature. The topological charge value of the OAM was used to identify the classification labels, and the ocean turbulence caused by different temperatures and salinity were analyzed. Experimentation results showed that the recognition accuracy for the OAM under the Laguerre–Gaussian beam rates of 1~5, 1~6, 1~7, 1~8, 1~9, and 1~10 was 98.93%, 98.89%, 97.33%, 96.66%, 95.40%, and 95.33%, respectively. The proposed method achieved a high recognition accuracy and performed efficiently under strong turbulence. Our research explored a new technique that provides a new idea for the demodulation of OAM in optical underwater communication.

Channel Polarization Scheme for Ocean Turbulence Channels in Underwater Visible Light Communication

“…When C 2 n = 1.0 × 10 −13 K 2 m −2/3 , the intensity distribution of the LG beam only retains partial annular distribution, the helical phase boundary of phase distribution shows obvious irregular jitter, and the distortion degree around the central singularity is significantly increased. In conclusion, OAM with a smaller value has stronger resistance to turbulence interference, which has also been verified by simulation through the support vector machine in a previous paper [26].…”

“…When C 2 n = 1.0 × 10 −13 K 2 m −2/3 , the intensity distribution of the LG beam only retains partial annular distribution, the helical phase boundary of phase distribution shows obvious irregular jitter, and the distortion degree around the central singularity is significantly increased. In conclusion, OAM with a smaller value has stronger resistance to turbulence interference, which has also been verified by simulation through the support vector machine in a previous paper[26].According to the spatial distribution of OAM and the performance of different modes of OAM in ocean turbulence, we determined the system block diagram, as shown in Figure4. BPSK modulation technology is used as the modulation module, because, compared with QPSK and 8PSK, BPSK has a lower transmission efficiency but stronger antinoise performance.…”

Top three intelligent algorithms for OAM mode recognitions in optical communications

“…Not only does it have a better generalization capability for nonlinear problems than traditional artificial neural networks, but it also avoids the inherent shortcoming of unstable training outputs of neural network [51]. Thus, its use is extensively reported in the field of portrait recognition [81], text classification [82], handwritten character recognition [83], and as well as OAM-based optical communication [84].…”

Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches

“…Additionally, these models can be used for nonparametric regression modeling, which allows them to capture complex relationships between variables. Furthermore, they can be tuned using bootstrap resampling techniques, which can provide a reliable method for determining the optimal free parameters of the algorithm [79].…”