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Pulse shapings employed in an underwater visible-light communication system based on carrierless amplitude and phase modulation utilizing a complex deep neural network equalizer
Optical Engineering ( IF 1.3 ) Pub Date : 2020-10-27 , DOI: 10.1117/1.oe.59.10.106110
Jiang Chen 1 , Nan Chi 1
Affiliation  

Abstract. We experimentally demonstrate the performance of a complex deep neural network (CDNN) equalizer in multi-band super-Nyquist carrier-less amplitude and phase (m-SCAP) modulation for underwater visible-light communications (UVLC). The in-phase and quadrature of the complex data after the match filter and down-sampling are combined as real number pairs and input to the CDNN, which outputs the real part and the imaginary part of the equalized complex data. We compare the different performances of three pulse shapings [better-than-Nyquist pulse shaping (BTN), square-root raised cosine (SRRC), and Xia] utilized in the m-SCAP UVLC system based on the CDNN. We demonstrate that the CDNN equalizer can outperform the traditional equalizer based on the Volterra series and least-mean-square algorithm. The experiments show that the low-roll-off BTN performs best, and the high-roll-off SRRC performs best in the m-SCAP system. In our experiment, the bit-error rate (BER) of the BTN system is 2.6 × 10 − 4, the BER of the SRRC is 3.6 × 10 − 4, and the BER of the Xia system is 7.3 × 10 − 4 when the spectral efficiency is 3.76 bit / s / Hz and the signal peak-to-peak voltage (Vpp) is 0.9 V.

中文翻译:

利用复杂深度神经网络均衡器基于无载波幅度和相位调制的水下可见光通信系统中采用的脉冲整形

摘要。我们通过实验证明了复杂深度神经网络 (CDNN) 均衡器在用于水下可见光通信 (UVLC) 的多频段超奈奎斯特无载波幅度和相位 (m-SCAP) 调制中的性能。将匹配滤波器和下采样后的复数数据的同相和正交组合为实数对输入到CDNN,CDNN输出均衡后的复数数据的实部和虚部。我们比较了基于 CDNN 的 m-SCAP UVLC 系统中使用的三种脉冲整形 [优于奈奎斯特脉冲整形 (BTN)、平方根升余弦 (SRRC) 和 Xia] 的不同性能。我们证明了 CDNN 均衡器可以优于基于 Volterra 级数和最小均方算法的传统均衡器。实验表明,在m-SCAP系统中,低滚降BTN表现最好,高滚降SRRC表现最好。在我们的实验中,BTN 系统的误码率 (BER) 为 2.6 × 10 − 4,SRRC 的 BER 为 3.6 × 10 − 4,而 Xia 系统的 BER 为 7.3 × 10 − 4,当频谱效率为 3.76 位/秒/赫兹,信号峰峰值电压 (Vpp) 为 0.9 V。
更新日期:2020-10-27
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