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Multiple-input multiple-output (MIMO) detectors have been a key technology in communication systems. In this paper, a new MIMO detector is designed by combining the adaptive learning rate (ALR) with the convolutional neural network (CNN) and successfully implementing it in a mode division multiplexing (MDM) optical transmission system. The results...
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Citations
... Another competitive neural network model, the convolutional neural network (CNN), has also been considered in our work. In previous simulation experiments [26], we tested the performance of CNN for MIMO detectors in MDM optical transmission systems. It is well known that CNNs are inconsistent with NNs and RNNs in the overall framework structure, hence in the following comparisons of the performance of different layers will be performed only between NNs and RNNs. ...
There is an increasing demand for data with the development of the world, and various fiber optic multiplexing techniques have become an important research direction to improve transmission capacity. However, the transmitted signals are subject to great interference due to mode coupling and mode dispersion, which require multiple-input multiple-output (MIMO) digital signal processing techniques to restore the quality of the transmitted signals. In this paper, a novel MIMO detector is designed using an adaptive learning recurrent neural network and successfully implemented in a mixed wavelength-division-mode-division-multiplexing (WDM-MDM) optical transmission system, and its performance is compared with that of the forced-zero detector and the minimum-mean-square-error detector. The results show that the introduction of an adaptive machine learning model in MIMO detection for WDM-MDM optical transmission systems can significantly improve the quality of the transmitted signals and achieve better performance than other MIMO detection algorithms while maintaining a faster computational speed and a lower number of parameters.
