## 5 Results and Discussion
### Performance Comparison  
Table 2 summarizes the performance comparisons in data rate and BER of our RGB-based OCC system with the proposed decoding algorithms and several recently proposed OCC systems. In RSE-based OCC systems, to increase the transmission distance and data rate, most researchers make an effort to modify the modulation and demodulation algorithms and replace white light LED with RGB-LED. In [8; 35], researchers adopt nonlinear fitting algorithms to equalize the distortion brought by the sampling offset, and they make progress in the performance of data rate and BER of the OCC system. In [36], this study utilizes ANN as an amplitude equalizer, but there is no significant improvement of data rate and BER performance. As the symbol rate goes faster, the performance deterioration caused by intensive ISI and ICI is inevitable in higher-speed OCC, which is mainly due to the sampling offset, which means that we need a much better decoding scheme and equalizer. In [5], this study proposes a Manchester-code-based decoding algorithm without sampling in mobile OCC, using CNN. However, owing to the disadvantages of Manchester code in utilizing RGB mode and the limited ability of the proposed CNN, it only adopts a single LED and has a data rate of about 2 kbit/s. Compared with the algorithm proposed in [15], in a similar experimental environment, under the circumstance without using an XOR compensation scheme, we achieve a data rate growth from 39 to 44.03 kbit/s. Actually, XOR compensation is not very meaningful to an OCC system, since it decodes much redundant data. In [15], 2D-CNN is just used for equalization after sampling by conventional algorithm, which means that it can be replaced by an ANN-based or SVM-based equalizer, and it is vulnerable to sampling offset caused by ISI. Giving the credit to higher-quality feature extraction and well-designed equalizer, the CAE-LSTM-Net proposed in this paper is not susceptible to interference, and it achieves a record-high data rate and the FEC requirement, combining feature extraction and equalization in an end-to-end way without sampling for decoding. Frankly speaking, our algorithm needs more computing resources because of its higher-complexity neural network, but it probably can be solved by special chips and circuits.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Symbol Rate (kbaud/s)** & **Kernels** & **Blocks** & **Hidden Numbers** & **LSTM Layers** \\ \hline
36 & 32 & 2 & 512 & 1 \\
40 & 32 & 3 & 512 & 1 \\
46 & 64 & 3 & 1024 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal parameters in compromising consideration.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Hardware** & **Technics** & **Data Rate** & **BER** & **Distance** & **Time** \\ \hline  & Manchester decoding, Block detection [23] & 1.1 kbit/s & No mention & 35 cm & 2012 \\  & OOK(Second-order & 0.896 kbit/s & 3.25 \(\times\)\(10^{-1}\) to & 25 cm & 2015 \\  & polynomial fitting) [8] & & 6.05 \(\times\)\(10^{-4}\) & & \\  & OOK(Polynomial fitting) [35] & 1.68 kbit/s & 3 \(\times\)\(10^{-3}\) to & 25 cm & 2015 \\  & & & 4 \(\times\)\(10^{-6}\) & & \\  & CSK(RGB,MIMO) [37] & 2.88 kbit/s & 3.16 \(\times\)\(10^{-3}\) & 10 cm & 2016 \\  & OOK(UFSOOK) [24] & 10.32 kbit/s & 1.01 \(\times\)\(10^{-4}\) & 20 cm & 2017 \\  & OOK(L1k) [38] & 0.78 kbit/s & Less than 3.8 \(\times\)\(10^{-3}\) & 1.5 cm & 2019 \\  & CSK(RGB channel separation) [39] & 0.96 kbit/s & Less than 3.8 \(\times\)\(10^{-3}\) & 2.5 cm & 2020 \\  & VAPPM [40] & 4.8 kbit/s & No mention & 4 m & 2015 \\  & OOK and FSK(RGB-MIMO,ANN) [36] & 1.2 kbit/s & 3.53 \(\times\)\(10^{-3}\) & 2.5 cm & 2021 \\  & OOK (XOR compensation and 2D-CNN equalizer) [15] & 43.7 kbit/s & 3.80 \(\times\)\(10^{-3}\) & 40 cm & 2019 \\  & Manchester code and CNN-based decoder and equalizer [5] & 2.16 kbit/s & 3.80 \(\times\)\(10^{-5}\) & 45 cm & 2020 \\  & This work & 44.03 kbit/s & 3.61 \(\times\)\(10^{-3}\) & 40 cm & 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison.