## 4 Experimental Results  
In this section, we first construct and optimize a traditional DNN-based waveform-to-symbol converter (DNN converter) in 64 APSK and 64 QAM UVLC systems. Based on it, a cascaded receiver consisting of a DNN converter with fewer taps or nodes and an NN-based DD-LMS algorithm is experimentally analyzed in detail. Figure 3 shows the relationship between the taps of traditional DNN-based waveform-to-symbol converter (DNN converter) and BER performance under different bias currents and signal Vpp, with 7% FEC of 3.8 \(\times\) 10\({}^{-3}\) as a BER threshold. Taps refer to the number of symbols corresponding to the input signal waveforms processed by the DNN converter at one time. Furthermore, the taps determine the number of inter-symbol interferences that the DNN converter can model. When the taps are less than 19, BER under different bias currents and signal, Vpp decreases rapidly as taps increase because it is not large enough to calculate the ISI. In this system, 19 taps allow a traditional DNN converter to achieve the best BER performance, which is clearly described in Figure 3. Because of the four times of upsampling, 19 taps mean that the input waveform length of the DNN converter is 76. However, as taps continue to increase, BER becomes worse. More taps lead to a more complex structure of DNN, requiring bigger data sets to train. Because of limited training data, overly complex networks cannot be trained better [19]. Therefore, there is a tradeoff between taps and the number of training data. The taps of 19 would be the best in our experiment. The constellation points of 19 under different bias currents and signal Vpp are displayed in Figure 3i-iv. Next, it is necessary to find the optimal structure of a traditional DNN converter. Figure 4a-c shows the relationship between BER performance and the number of hidden layers, nodes of each hidden layer, and the activation functions of traditional DNN converter under different signal Vpp, respectively. The bias current is fixed at 150 mA, and signal Vpp varies from 250 mV to 850 mV. In Figure 4a, the number of nodes in each hidden layer is set as 32. The DNN converters with different hidden layers ranging from 1 to 5 all outperform traditional CAP demodulation, especially in the high signal Vpp region. Thus, DNN-based waveform-to-symbol converters are experimentally demonstrated to be an effective scheme to improve the communication performance of UVLC systems. When the number of hidden layers increases from 1 to 3, the corresponding BER decreases. This is because the DNN converter requires a large enough complexity to  
Figure 3: The relationship between BER performance and the taps of traditional DNN-based waveform-to-symbol converter under different bias currents and signal Vpp. (**i**-**iv**) show the constellation points of 19 taps under different bias currents and signal Vpp respectively. perfectly convert the signal waveforms into the corresponding symbols. As the number of hidden layers increases from 1 to 3, the DNN converter becomes more complex and fits the relationship between input and output more fully. However, as the number of hidden layers continues to increase from 3 to 5, the BER performance has not been further improved but has deteriorated somewhat. Because three hidden layers are sufficient to fit the nonlinear relationship between input and output, and no more layers are required. Instead, more hidden layers consume more computing resources and may lead to overfitting. Therefore, the number of hidden layers is chosen as 3. In Figure 4b, the DNN converter has three hidden layers with \(n_{1},n_{2},n_{3}\) nodes respectively, so it is referred to as DNN (\(n_{1},\,n_{2},\,n_{3}\)). The BER performance of DNN (32, 32, 16), DNN (96, 64, 32), DNN (96, 96, 96) and DNN (128, 128, 128) are shown, compared with traditional CAP demodulation. DNN (96, 96, 96) achieves better performance than DNN (32, 32, 16) and DNN (96, 64, 32) due to greater complexity.