Deep neural network has been used to compensate the nonlinear distortion in the field of underwater visible light communication (UVLC) system. Considering the tradeoff between the equalization performance and the network complexity is the priority in practical applications. In this paper, we propose a novel hybrid frequency domain aided temporal convolutional neural network (TFCNN) with attention scheme as the post-equalizer in CAP modulated UVLC system. Experiments illustrate that the proposed TFCNN can achieve better equalization performance and remain the bit error rate (BER) below the 7% hard-decision forward error correction (HD-FEC) limit of 3.8×10⁻³ when other equalizers loss effectiveness under serious distortion condition. Compared with the standard deep neural network, TFCNN shows 76.4% network parameters complexity reduction.

中文翻译：

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低复杂度的混合频域辅助时间卷积神经网络在UVLC系统中的应用

深度神经网络已用于补偿水下可见光通信（UVLC）系统领域中的非线性失真。考虑均衡性能和网络复杂度之间的折衷是实际应用中的优先级。在本文中，我们提出了一种新的混合频域辅助时域卷积神经网络（TFCNN），其具有注意方案作为CAP调制UVLC系统中的后均衡器。实验表明，所提出的TFCNN可以实现更好的均衡性能，并且将误码率（BER）保持在3.8×10 ^-3的7％硬判决前向纠错（HD-FEC）限制以下当其他均衡器在严重失真的情况下会失去有效性。与标准的深度神经网络相比，TFCNN的网络参数复杂度降低了76.4％。