## 4 Experiments and Analysis of Results
### The Analysis of Downlink NOMA-VLC  
In the downlink NOMA-VLC system, the BER performance for different bias currents, attenuation values, and power allocations was analyzed. It can be seen from Figure 13 that  
Figure 11: BER performance of NOMA-VLC under different attenuation values. Figure 12: BER performance of two users with Material-2 for various power allocation ratios. with the various bias current values, the BER increased, and the optimal value was obtained at 0.22 A. When the attenuation value was set from 2 to 11 dB, a higher attenuation value led to better BER performance, and the best BER performance was obtained at 11 dB. Figure 14 shows the BER performance of the NOMA-OOK system for different distance variations with LS-SIC and LSTM-SIC detection algorithms under the conditions of optimal bias current and attenuation values. It can be seen that the traditional SIC, LS-SIC, and LSTM-SIC detection algorithms changed the BER at different distances for each user. Since the power allocation of the two users was different, the signal of strong user was detected directly with a low level of interference. However, when the weak user was detected, the BER performance sharply deteriorated with the elimination of more interference of the strong user inaccurately. Therefore, the BER of User 1 was better than that of User 2. However, after introducing interference estimation coefficients to accurately reconstruct the interference, the BER performance of User 2 became better. Furthermore, when the transmission distance was less than 5.5 m, the BERs of both users were lower than the FEC limit when using LS-SIC and LSTM-SIC detection algorithms. As the distance continued to increase to 7.5 m, the BERs of User 1 with both algorithms were still lower than the FEC limit, while the BERs of User 2 were lower than the FEC limit when the distance was within 6 m. When the distance exceeded 6 m, the BER of User 2 with the LS-SIC algorithm was higher than the FEC limit, while the BER with the LSTM-SIC algorithm was still lower than the FEC limit up to 7.5 m. Therefore, the interference estimation performance was optimized by using the LSTM to estimate the interference coefficient with the training sequence. In addition, insets are the eye diagrams (a, b, c, d) for the corresponding BERs indicated by the arrows. The eye diagram of User 1 with equalization is represented by (a). The open eyes indicate that this signal is suitable for demodulation. The eye diagram of User 2 with equalization is represented by (b). We can see that the eyes are invisible, which indicates the signal is not suitable for demodulation. Next, we used the LS-SIC algorithm to improve the quality of User 2, as shown in eye diagram (c). Here, the open eyes indicate the performance improvement. We also considered LSTM-SIC to further improve the performance of User 2, as shown in eye diagram (d). We can clearly see that the eyes are wide open, resulting in better performance, and the signal is suitable for demodulation. Figure 13: Original BER curve without interference estimation optimization. As shown in Figure 15, the BER performance of different interference reconstruction algorithms was further compared considering different power allocation ratios. It can be seen that when the power allocation of the two users changed, the BERs of both users decreased until the allocation ratio was 0.8. When the ratio was larger than 0.8, the BER of the original algorithm for the strong user (User 1) decreased sharply, and the BER of the weak user (User 2) deteriorated sharply and exceeded the FEC. The BER of User 1 reached a minimum value at the power allocation ratio of 0.95, which was lower than the FEC. After using the LS-SIC and the LSTM-SIC detection algorithms, both algorithms improved the BER of User 2 to satisfy the FEC limit for a power allocation ratio ranging between 0.8 and 0.97. As the power allocation ratio increased to 1, which is equivalent to single-user transmission, the BER became worse again.