## 4 Experimental Results
Instead, DNN with too complex a structure easily leads to overfitting, which could be verified by DNN (128, 128, 128). In Figure 4c, the effects of different activation functions (Sigmoid, ReLU and Tanh) and no activation functions (None) have been investigated. The DNN converter with the activation function of Tanh has the best performance, then ReLU and Sigmoid. The DNN converter with no activation function has the worst performance that is similar to traditional CAP demodulation. Therefore, the DNN-based waveform-to-symbol converter we optimize has three hidden layers with 96, 96 and 96 nodes, and Tanh is employed as the activation function. Figure 5 presents the relationship between the BER performance of different taps and epochs during training, with 7a % FEC of 3.8 \(\times\) 10\({}^{-3}\) as a BER threshold. The bias current is 150 mA, and the signal Vpp is set as 550 mV. When the epoch is less than 200, the BER of the DNN converter decreases below the threshold rapidly. As the epoch continues to increase, BER drops slowly and converges gradually. It can be seen that the BER using a DNN converter with the taps of 19 is lower than 11 and 27 when training is finished, which is consistent with the results in Figure 3. Figure 5i-iv displays the constellation points obtained by the DNN converter with the taps of 19 when the epoch is 2, 50, 100, and 500, respectively. As the epoch increases, the constellation points become more and more clear and distinguishable. Then the effects of the DNN converter on BER in 64 QAM or 64 APSK UVLC systems under different bias currents and signal Vpp are investigated, compared with traditional CAP demodulation. The results are presented in Figure 6, where the dynamic range of signal Vpp and bias current is circled by a black line of 2.0 \(\times\) 10\({}^{-3}\) FEC threshold. The bias current varies from 70 mA to 170 mA, and the signal Vpp varies from 250 mV to 750 mV. Comparing Figure 6a with Figure 6c, it can be seen that 64 APSK systems work mainly in the higher current region where signals suffer from severe nonlinearity, which means that 64 APSK is more advantageous to resist nonlinearity and it could obtain higher SNR in the circumstances of strong attenuation in UVLC systems. The DNN converter could  
Figure 4: The relationship between BER performance and (**a**) the number of hidden layers; (**b**) nodes of each hidden layer; (**c**) the activation functions of traditional DNN converter under different signal Vpp. significantly extend the dynamic range both in 64 APSK and 64 QAM systems, especially in high signal Vpp region, because of its powerful ability to handle nonlinearity. However, in low signal Vpp region, BER performance using a DNN converter is similar to that using traditional CAP demodulation in 64 APSK or 64 QAM systems. This can be predicted because when signal Vpp is relatively low, noise plays a dominant role in the deterioration of communication performance, and the neural network is proven to be almost unable to handle it. In order to analyze the effects of the DNN converter, more specifically, the BER versus different signal Vpp for 64 APSK and 64 QAM UVLC systems under the bias current of 150 mA is demonstrated in Figure 7, compared with traditional CAP demodulation. In terms of traditional CAP demodulation (black curves in Figure 7a,b), the BER decreases as the increase of signal Vpp when it is smaller than 450 mV due to the enhancement of SNR. When it is larger than 450 mV, the BER versus signal Vpp has an opposite tendency because of nonlinear distortions. Thus, the signal Vpp dynamic range of signal Vpp is 250 mV for 64 APSK and 180 mV for 64 QAM with 7% FEC of 3.8 \(\times\) 10\({}^{-3}\), respectively. The communication performance is significantly improved by using DNN converter (red curves in Figure 7a,b), as the signal Vpp dynamic range enlarges by 104% (from 250 mV to 511 mV) for 64 APSK in Figure 7a and 181% (from 180 mV to 506 mV) for 64 QAM in Figure 7b.