## 5 Experimental Results and Discussion  
In this section, we compare the performance of the proposed AE-8 QAM with the Norm-8 QAM and PO-8 QAM in the real UVLC system. Before starting the performance comparison, we first analyze the convergence of the neural-network-based channel model and autoencoder in our AEGSM framework. Figure 7 illustrates the learning curve for training loss and test loss of the channel model and autoencoder during symbol-wise optimization. We can see that the training and testing loss of the channel and autoencoder decrease quickly. In order to ensure that the training of the channel model and autoencoder can fully converge, we set the epoch of both of them to 100. Figure 6: Experimental setup of UVLC system with a green LED. Figure 7: Learning curve of the channel model and autoencoder. On the basis of training the AE, we further optimize the constellation distribution output by the encoder at the bit-wise level and then deploy it to the real system for performance testing. Figure 8 shows the BER performance of the Norm-8 QAM, PO-8 QAM, and AE-8 QAM versus Vpp in a real UVLC system. Additionally, to verify the performance improvement brought about by bit-wise optimization, we also plot the BER performance of the AE-8 QAM without the RPO algorithm simultaneously. As Vpp increases from a small value, the BER of all shaping schemes decreases until the optimal operating point is reached. When the Vpp is too large, the signal suffers from strong nonlinearity and is damaged seriously, so after the optimum point, the BER increases with Vpp. Figure 9 illustrates the constellation points of all shaping schemes received under different Vpp values, which shows that the signal will be greatly damaged if the Vpp is too large or too small. Compared with other shaping methods, the AE-8 QAM has always achieved the best performance due to its strong adaptive ability and anti-nonlinearity. At the same time, the performance gap between the AE-8 QAM without the RPO algorithm and the AE-8 QAM verifies the gains brought about by bit-wise optimization. Furthermore, to comprehensively explore the gains of different shaping schemes under various equalization algorithms, three different symbol-wise post-equalization algorithms are employed under the same experimental platform to perform symbol-wise post-equalization: LMS equalization, Volterra equalization, and NN-based equalization. Before performing symbol-level post-equalization, all received signals are processed with wave-wise Volterra equalization to obtain the optimal performance. The Q factors of different shaping schemes under different post-equalization procedures are shown in Figure 10. It is worth noting that the Q factor of the AE-8 QAM after NN-based equalization is only increased by 0.30 dB compared with that after LMS post-equalization, which is lower than the Nom-8 QAM's 0.90 dB and PO-8 QAM's 0.46 dB. This also confirms that the AE-8 QAM itself has a certain anti-nonlinear ability, so the gain of the AE-8 QAM between using NN-based post-equalization and LMS post-equalization is not obvious. However, since the Norm-8 QAM does not have an adaptive ability or anti-nonlinearity, there will be a more obvious performance improvement after using NN-based post-equalization than that seen when using LMS post-equalization. After investigating the BER performance and Q factor gains, we fix the bias and amplitude of each shaping scheme to their optimal values and test their performance at different transmission rates. Figure 11 represents the performance of the three schemes under different bit rates, in which we can find a 103 Mbps improvement of the AE-8 QAM compared with the Norm-8 QAM at the 7% HD-FEC threshold. The AE-8 QAM optimized by our AEGSM framework achieves highest transmission speed of 2.05 Gbps in the real UVLC system. Throughout the experiment, the roll-off factor of the shaping filter is set to 0.205. The carrier frequency is 407 MHz, and the bandwidth is 801 MHz when the transmission rate achieves 2G.