Adaptive Bayes-Adam MIMO Equalizer With High Accuracy and Fast Convergence for Orbital Angular Momentum Mode Division Multiplexed Transmission

Wang, Sihan; Gao, Ran; Xin, Xiangjun; Zhou, Sitong; Chang, Huan; Li, Zhipei; Wang, Fu; Guo, Dong; Yu, Chao; Liu, Xinyu; Dong, Ze; Zhang, Han; Wang, Fei

doi:10.1109/jlt.2023.3250969

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…The increasing demand for super-large data storage, transmission and sharing services has further driven the explosive growth of network traffic. Therefore, modern communication networks need higher transmission rates, larger transmission capacity and better transmission quality, to ensure the growing demand of network traffic [1][2][3][4][5]. Improving the capacity of optical fiber communication systems has become an urgent issue for economic and social development [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Pruned Convolutional Neural Network Based Nonlinear Equalizer in Coherent Optical Communication Systems

Liu

Jiang

et al. 2023

Electronics

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Nonlinear impairments caused by devices and fiber transmission links in a coherent optical communication system can severely limit its transmission distance and achievable capacity. In this paper, we propose a low-complexity pruned-convolutional-neural-network-(CNN)-based nonlinear equalizer, to compensate nonlinear signal impairments for coherent optical communication systems. By increasing the size of the effective receptive field with an 11 × 11 large convolutional kernel, the performance of feature extraction for CNNs is enhanced and the structure of the CNN is simplified. And by performing the channel-level pruning algorithm, to prune the insignificant channels, the complexity of the CNN model is dramatically reduced. These operations could save the important component of the CNN model and reduce the model width and computation amount. The performance of the proposed CNN-based nonlinear equalizer was experimentally evaluated in a 120 Gbit/s 64-quadrature-amplitude-modulation (64-QAM) coherent optical communication system over 375 km of standard single-mode fiber (SSMF). The experimental results showed that, compared to a CNN-based nonlinear equalizer with a 6 × 6 normal convolutional kernel, the proposed CNN-based nonlinear equalizer with an 11 × 11 large convolutional kernel, after channel-level pruning, saved approximately 15.5% space complexity and 43.1% time complexity, without degrading the equalization performance. The proposed low-complexity pruned-CNN-based nonlinear equalizer has great potential for application in realistic devices and holds promising prospects for coherent optical communication systems.

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Section: Introductionmentioning

confidence: 99%

Low-Complexity Pruned Convolutional Neural Network Based Nonlinear Equalizer in Coherent Optical Communication Systems

Liu

Jiang

et al. 2023

Electronics

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Power-aided probabilistic shaping parameter optimization for long-haul optical fiber transmission

Zhao,

Zhang,

et al. 2024

Appl. Opt.

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A power-aided probabilistic shaping (PS) parameter optimization scheme is proposed to localize the optimal transmitting setup and maximize the transmission capacity in long-haul optical fiber transmission. Using a neural network and genetic algorithm (NNGA), the proposed scheme can select the optimal transmission parameter set, including the launching optical power (LOP) and three PS characteristics. We built a testbed using MATLAB and VPI to validate the proposed scheme. The proposed scheme was demonstrated in a dual-polarization coherent transmission system. The results show that the proposed scheme improves the generalized mutual information (GMI) by 0.3035 bits/symbol/pol and normalized GMI by 0.1022 in a 1000 km G.654E fiber transmission at 420-Gbit/s, compared to a traditional MB-based PS technique. The signal-to-noise ratio (SNR) achieves a gain of 1.003 dB, which validates the outperformance of the proposed scheme.

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Equalization system of low differential mode delay few-mode fibers based on the neural network MIMO algorithm

Guo,

Yan,

Ren

et al. 2024

Opt. Express

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In recent years, with the development of information networks, higher requirements for transmission capacity have been recommended. Yet, at the same time, the capacity of single-mode fiber is rapidly approaching the theoretical limit. The multidimensional multiplexing technique is an effective way to solve this problem. Since the high differential mode delay (DMD) of transmission fiber increases the complexity of demultiplexing in equalization algorithms, we use an intelligent design method to optimize the trench-assisted gradient refractive index structure in this paper. The maximum DMD of the optimized optical fiber structure is 19.6 ps/km. A least mean squares-feedforward neural network constant modulus algorithm (LMS-FNNCMA) is also designed by using the theory of the least mean squares (LMS), constant modulus algorithm (CMA), and the multiple input multiple output (MIMO) neural networks. In order to verify the accuracy of the algorithm, a polarization division multiplexing-wavelength division multiplexing-mode division multiplexing (PDM-WDM-MDM) optical transmission system is constructed through simulation. The algorithm successfully realizes the de-crosstalk over a transmission distance of 1200 km at a rate of 1.2 Tbps under simulation conditions.

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Adaptive Bayes-Adam MIMO Equalizer With High Accuracy and Fast Convergence for Orbital Angular Momentum Mode Division Multiplexed Transmission

Cited by 6 publications

References 25 publications

Low-Complexity Pruned Convolutional Neural Network Based Nonlinear Equalizer in Coherent Optical Communication Systems

Low-Complexity Pruned Convolutional Neural Network Based Nonlinear Equalizer in Coherent Optical Communication Systems

Power-aided probabilistic shaping parameter optimization for long-haul optical fiber transmission

Equalization system of low differential mode delay few-mode fibers based on the neural network MIMO algorithm

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