# An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications
## 4 Simulations and Measured Results
Through the bias tee (Tektronix PSPL5575A), the generated waveform signal was superimposed onto the bias current to drive the adopted LED (Crescent D01-A1-A33). On the basis of the measured data of the LED in Table 1, the bias current was set to 60 mA and the mean of the generated waveform signal was set to 5 volts, and these settings caused the operation point of the LED to be 300 mW in order to introduce the nonlinearity effect into the LED emitting optical signal. Figure 10: BER in AWGN versus \(E_{b}/N_{0}\) for the NLMS, the Chebyshev polynomial, and the proposed ANN predistortion with 64 QAM. Figure 9: BER in AWGN versus \(E_{b}/N_{0}\) for the NLMS, the Chebyshev polynomial, and the proposed ANN predistortion with 16 QAM. A PD (THORLABS PDA10A-EC), with a 150 MHz bandwidth, 1 mm\({}^{2}\) active area, and \(5\times 10^{-3}\) V/A transimpedance gain, was utilized to direct detect the light intensity. To conduct the A/D conversion, the PD output was captured by using a mixed signal oscilloscope (MSO) (PicoScope 3205D), and then followed by a process of downsampling. Finally, the sampled signal was further subjected to packet synchronization, demodulation, and BER analysis. Figure 12 shows constellation diagrams of 16 QAM and 64 QAM modulation under the condition that the distance between the LED and the PD was 40 cm. Obviously, the constellation diagrams with the ANN predistorter are much clearer than those with either the NLMS or Chebyshev polynomial predistorters. This observation was also validated by the error vector magnitude (EVM) results, which are shown in the titles of each subfigure. Moreover, the BERs of the considered LED predistortion algorithms plotted against the distances between the LED and the PD for the cases of 16 QAM (\(M=16\)) and 64 QAM (\(M=64\)) are shown in Figures 13 and 14, respectively. As can be seen in Figure 13, the experimental results verified that the BER of the 16 QAM modulated OFDM system with the proposed ANN predistorter was significantly superior to those with the NLMS and the Chebyshev polynomial predistorters. As for the case of the 64 QAM-modulated OFDM signals shown in Figure 14, the BER of the system with the proposed ANN predistorter still outperformed those with the NLMS and the Chebyshev polynomial predistorters. In summary, the experimental results validated the view that the proposed ANN predistorter was capable of adequately compensating for LED nonlinearity, and hence achieving better system performance compared to the NLMS and the Chebyshev polynomial predistorters. Figure 11: Experimental setup of the DCO-OFDM VLC testbed. Figure 12: Constellation diagrams of 16 QAM and 64 QAM modulation. (**a**–**c**) are constellation diagrams of the 16 QAM modulated OFDM signal with the NLMS, Chebyshev polynomial, and the proposed ANN predistorters, respectively. (**d**–**f**) are constellation diagrams of the 64 QAM modulated OFDM signal with the NLMS, Chebyshev polynomial, and the proposed ANN predistorters, respectively. Figure 5:0 BER versus the distance between the LED and the PD of the 16 QAM modulated OFDM for the NLMS, the Chebyshev polynomial, and the proposed ANN predistortion.