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• W4313215263 abstract "Orbital angular momentum shift-keying (OAM-SK), which enables modulation of digital signals by rapidly switching OAM modes, is promising in improving the modulation ability and confidentiality. Robust identification of conjugate OAM modes of VBs perturbed by atmospheric turbulence is critical for efficient demodulation of OAM-SK signals. We experimentally investigate a convolutional neural network (CNN) approach to detect OAM modes from diffraction patterns of turbulence-induced vortex beams. By introducing the hybrid-turbulence with variable turbulence strengths, this approach possessed high robustness against turbulence and low computational complexity in conjugate OAM mode identification, achieved 99.53 % mode detection accuracy in the −8 to 8 range for the hybrid-turbulence of Cn2=10-14∼10-12m-2/3. Mapping and encoding a grayscale image to the OAM modes, an OAM-SK communication link was built and the demodulated bit-error-rate (BER) was only 6.85 × 10−3. We show that this approach also can effectively assist in demodulating the conventional modulation signals, promoting the communication transmission capacity. By combining OAM-SK with on–off keying (OOK) and quadrature phase shift-keying (QPSK) to construct joint-modulation links, and OOK (QPSK) signals were successfully demodulated with the BER of 8.2 × 10−9–9.1 × 10−3 (3.34 × 10−6–2.8 × 10−3) with the assistance of CNN. Our results indicate that the proposed robust CNN is not only effective for demodulating OAM-SK signals, but is also compatible with conventional modulation signals, which is very promising for OAM-SK communication applications that require OAM mode identification." @default.
• W4313215263 created "2023-01-06" @default.
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• W4313215263 date "2023-04-01" @default.
• W4313215263 modified "2023-09-27" @default.
• W4313215263 title "Robust neural network-assisted conjugate orbital angular momentum mode demodulation for modulation communication" @default.
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• W4313215263 doi "https://doi.org/10.1016/j.optlastec.2022.109013" @default.
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