Two tributaries heterogeneous neural network based channel emulator for underwater visible light communication...

This paper proposes a novel two tributaries heterogeneous neural network (TTHnet) based channel emulator, which is suitable for both estimating single-carrier and multi-carrier modulated channels of underwater visible light communication (UVLC). Compared to traditional neural networks, the TTHnet channel emulator has only 1932 trainable parameters, which is only 0.8% of multilayer perceptron (MLP) based channel emulator and 1% of a convolutional neural network (CNN) based channel emulator. Furthermore, it provides a more accurate estimation of the UVLC channel and greater interpretability than MLP and CNN. The experiments in this paper use carrier-less amplitude/phase modulation (CAP) and discrete multi-tone modulation (DMT) as representative examples of single-carrier and multi-carrier modulation, respectively. The experiment proves that the TTHnet based channel emulator could effectively emulate the channel response of UVLC systems both in time and frequency domain. To the best of our knowledge, this is the first time that the single-carrier and multi-carrier modulated UVLC channel is emulated by the deep neural networks based channel emulator, which will effectively accelerate the research progress of UVLC and reduce research costs of UVLC systems.