Optimized OAM Laguerre-Gauss Alphabets for Demodulation using Machine Learning

In this paper, we develop new classification and estimation algorithms in the context of free space optics (FSO) transmission. Firstly, a new classification algorithm is proposed to address efficiently the problem of identifying structured light modes under jamming effect. The proposed method exploits support vector machine (SVM) and the histogram of oriented gradients algorithm for the classification task within a specific range of signal-to-jamming ratio (SJR). The SVM model is trained and tested using experimental data generated using different modes of the structured light beam, including the 8-ary Laguerre Gaussian (LG), 8-ary superposition-LG, and 16-ary Hermite Gaussian (HG) formats. Secondly, a new algorithm is proposed using neural networks for the sake of predicting the value of SJR with promising results within the investigated range of values between −5 dB and 3 dB.

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Optimized OAM Laguerre-Gauss Alphabets for Demodulation using Machine Learning

Cited by 2 publications

References 6 publications

A Survey on Decoding Orbital Angular Momentum Optical Communication Beams

A Survey on Decoding Orbital Angular Momentum Optical Communication Beams

Structured Light Transmission under Free Space Jamming: An Enhanced Mode Identification and Signal-to-Jamming Ratio Estimation Using Machine Learning

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