Abstract
In this paper, we propose a low-complexity frequency slicing deep neural network (FSDNN) for wide-band signal post-equalization in a 1.2 m underwater visible light communication system. FSDNN and deep neural network (DNN) outperform the least mean square equalizer. Then, by splitting the received signal into two parallel signals using a digital low-pass filter and a high-pass filter, we demonstrate that the FSDNN significantly reduces the complexity of the traditional DNN post-equalizer. Moreover, the complexity of the FSDNN decreases considerably to 11.15% compared with the conventional DNN for a 2.7 Gbit/s wide-band transmitted signal with a similar bit error ratio performance.
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Acknowledgements
This work was partially supported by National Key Research and Development Program of China (Grant No. 2017YFB0403603) and Natural National Science Foundation of China (Grant No. 61925104).
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Chi, N., Hu, F., Li, G. et al. AI based on frequency slicing deep neural network for underwater visible light communication. Sci. China Inf. Sci. 63, 160303 (2020). https://doi.org/10.1007/s11432-020-2851-0
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DOI: https://doi.org/10.1007/s11432-020-2851-0