## 4 Experimental Results
Meanwhile, the Q factor of 950 mA biases current increases by 3.12 dB for 64 APSK and 2.96 dB for 64 QAM, compared with traditional CAP demodulation. It is experimentally verified that the DNN converter has obvious advantages over traditional CAP demodulation in the nonlinear region, thus more suitable for UVLC systems. We also measured the Q factor for 64 APSK and 64 QAM UVLC systems at different bit rates under the optimal bias voltage and signaled Vpp with 7% FEC of 3.8 \(\times\) 10\({}^{-3}\) as a BER threshold. The performance of the DNN converter is in comparison with traditional CAP demodulation in Figure 8. As expected, the DNN converter could achieve greater communication capacity. To be specific, the highest bit rate is 3.23 Gb/s in the 64 APSK system utilizing DNN converter and 3.09 Gb/s in 64 QAM system, which is 125 Mb/s and 50 Mb/s faster than traditional CAP demodulation, respectively. 64 APSK using the DNN converter is experimentally proved to be a promising scheme for future high-speed UVLC systems. Figure 5: The relationship between BER performance of different taps and epoch during training. (**i**-**iv**) show the constellation points when epoch is 2, 50, 100 and 500, respectively. At this point, the traditional DNN-based waveform-to-symbol converter we optimize is obtained. However, it is of high complexity to sustain the pressure of mitigating ISI, linear and nonlinear distortions, which is not expected for the implementation of UVLC systems. In order to achieve a better tradeoff between communication performance and computation complexity, we innovatively propose a cascaded receiver consisting of a DNN-based waveform-to-symbol converter and an NN-based DD-LMS algorithm. The DNN converter at the front stage is generated by appropriately reducing the taps or the nodes of hidden layers of the DNN converter we optimize above. It could still mitigate the majority of ISI and distortions of received signals. Then, a modified NN-based DD-LMS is utilized for further improving the quality of complex symbols (constellation points) produced by the DNN converter at the front stage. Thus, the algorithm could achieve similar performance to the traditional DNN converter we optimize but with much lower complexity. Figure 6: The contour of BER versus different bias currents and signal Vpp for (**a**) 64 APSK, traditional CAP demodulation; (**b**) 64 APSK, DNN converter; (**c**) 64 QAM, traditional CAP demodulation; (**d**) 64 QAM, DNN converter. Figure 7: BER versus different Vpp for (**a**) 64 APSK; (**b**) 64 QAM UVLC systems. In order to investigate the effects of modified NN-based DD-LMS on constellation points, we first pay attention to an important metric, which is phase offset. It is the average phase offset of each constellation point's class compared with standard constellation points. The results of 64 APSK and 64 QAM systems are shown in Figure 9a,b, respectively. The bias current is fixed at 150 mA, and signal Vpp varies from 250 mV to 950 mV. Based on the traditional DNN converter we optimize above, the front-stage DNN converter is obtained by reducing the taps from 19 to 11 and the nodes of three hidden layers from 96 to 32. As can be seen from Figure 9, the phase offsets after the front-stage DNN converter are still high due to remaining ISI and distortions. Fortunately, the phase offsets could be significantly reduced by modified NN-based DD-LMS, which indicates the improvement of communication performance. Figure 9i,ii show the movement vector of each constellation points' center after NN-based DD-LMS for 64 APSK and 64 QAM systems when signal Vpp is 550 mV, respectively. In addition to phase offset, we measured the average distance between the received constellation points' center and standard constellation points. Moreover, a new metric named \"Average Cluster Sum of Square\" (ACSS) is proposed to represent how concentrated  
Figure 8: BER versus different Bit rate for 64 APSK and 64 QAM UVLC systems.