Confucianism, as a philosophy, has performed an important role in China's business history[1]. It would be a mistake, however, to assume that Confucianism was popular from its outset. Kong Fu Ze, called Confucius by the Jesuit missionaries, lived from 551-479 BC. However, it was more than 300 years after his death before his philosophy found acceptance. The fifth Han Emperor, Wu (147 BC) found Confucianism well suited to the conditions of ancient China[2]. Strong sense of hierarchy The centralized monarchy, which dominated China for 2,000 years, survived by maintaining an immense hierarchical system. The long existence of this system

We report successful transmission of dual-LP(11) mode (LP(11a) and LP(11b)), dual polarization coherent optical orthogonal frequency-division multiplexing (CO-OFDM) signals over two-mode fibers (TMF) using all-fiber mode converters. Mode converters based on mechanically induced long-period grating with better than 20 dB extinction ratios are realized and used for interfacing single-mode fiber transmitter and receivers to the TMF. We demonstrate that by using 4×4 MIMO-OFDM processing, the random coupling of the two LP(11) spatial modes can be successfully tracked and equalized with a one-tap frequency-domain equalizer. We achieve successful transmission of 35.3 Gb/s over 26-km two-mode fiber with less than 3 dB penalty.

With ideal nonlinearity compensation using digital back propagation (DBP), the transmission performance of an optical fiber channel has been considered to be limited by nondeterministic nonlinear signal-ASE interaction. In this paper, we conduct theoretical and numerical study on nonlinearity compensation using DBP in the presence of polarization-mode dispersion (PMD). Analytical expressions of transmission performance with DBP are derived and substantiated by numerical simulations for polarization-division-multiplexed systems under the influence of PMD effects. We find that nondeterministic distributed PMD impairs the effectiveness of DBP-based nonlinearity compensation much more than nonlinear signal-ASE interaction, and is therefore the fundamental limitation to single-mode fiber channel capacity.

Deeping learning can achieve high parallelism and robustness, which is especially suitable for massive multiple-input multiple-output (MIMO) detection. There are already some well-developed deep learning models applied to MIMO detection, in which detection network is a typical representative model with excellent performance, but its complexity is high. This paper aims to simplify the detection network model, and the simplification runs through the entire data processing. This simplification includes three improvements. First, the number of inputs is reduced to simplify inputs; Second, the network connection structure is simplified by changing network from full connectivity to sparsely connectivity and reducing the number of network layers by half. Third, the loss function optimizes to avoid irreversible problems with the matrix. Base on the above improvements, the complexity of the network is reduced from <em>O(64n²)</em>&nbsp;to <em>O(3n)</em>. The simulation results indicates that the proposed structure has better performance than the existing detection network.

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In this paper we experimentally demonstrate transmission performance of optical DFT-spread OFDM systems in comparison with conventional OFDM systems. A 440.8-Gb/s superchannel consisting of 8 x 55.1-Gb/s densely-spaced DFT-S OFDM signal is successfully received after 1120-km transmission with a spectral efficiency of 3.5 b/s/Hz. It is shown that DFT-S OFDM can achieve an improvement of 1 dB in Q factor and 1 dB in launch power over conventional OFDM. Additionally, unique word aided phase estimation algorithm is proposed and demonstrated enabling extremely long OFDM symbol transmission.

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