A joint constellation shaping (JCS) three-dimensional (3D) 16-ary modulation scheme constructed with a pair of common-bottomed trigonal cones (CBTC) as primitives is proposed. Compared to the 3D traditional constellation (TC) and the 3D geometric constellation shaping (GS) structure previously proposed by our group (GGS), the constellation figure of merit (CFM) is improved by 0.3906 and 0.
View Article and Find Full Text PDFIn this paper, we propose a high-security space division multiplexing optical transmission scheme based on constellation grid selective twisting, which adopts the Rossler chaos model for encrypting PDM-16QAM signals, being applied to a multicore, few-mode multiplexing system. The bitstream of the program is passed through XOR function before performing constellation grid selective twisting and rotation of the constellation map to improve the security of the system. The proposed system is verified experimentally by using 80-wave and 4-mode multiplexing in one of the 19-core 4-mode fibers.
View Article and Find Full Text PDFIn this paper, we propose a high-security three-dimensional optical transmission system utilizing time-frequency-space interleaving chaos, which simultaneously enhances the reliability and security of the system. The four-wing 3D chaos model encrypts the time-frequency space interleaved modulation domain of a orthogonal time-frequency space (OTFS) modulation signal and the modulated phase information simultaneously, improving the system's security. We also experimentally validate the proposed high-security 3D-OTFS method, utilizing the hexadecimal modulation technique.
View Article and Find Full Text PDFWe report a low-complexity and high-security orthogonal chirp division multiplexing (OCDM) transmission scheme based on generative adversarial networks (GAN) enhanced chaotic encryption. Our investigation focuses on the security and efficiency of the communication system. To successfully apply GAN for the encryption scheme, we design our networks with new network architectures and modify the loss functions to improve the adversarial training performance of the networks.
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