# BigGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication
## 4 Results and Discussion  
Based on the theoretical study of the VLC system based on OFDM in Section 2, the experimental platform, as shown in Figure 4, was built in this paper, and the transmission performance of 4 QAM-DCOOFDM on the VLC link of commercial LED was numerically studied, and the feasibility of using the equalizer with deep learning structure for digital compensation of the channel was verified. OFDM signals were generated in MATLAB and transmitted through an arbitrary waveform generator (AWG). A set of \(X(K)\) lengths was set to 64, corresponding to an _FFT_ length \(N\_fft\) of \(2\times 64+2\), a cyclic prefix \(N\_cp\) of \(1/4\) of the \(X(K)\) length, and a full OFDM symbol length of \(N\_fft+N\_cp\). The bit rate of symbols emitted by AWG was set to 100 mbps, the bias voltage of the LED was set to 6 V, and the drive voltage of the signal was set to 1 V. At the receiving end, a commercial PD was used at a distance of 3 m from the LED to receive the light signal and perform the photoelectric conversion. The signal was then acquired by an oscilloscope and demodulated in MATLAB. The BER of three different receiving methods in Section 2 under different signal-to-noise ratios (SNR) were compared, and no forward error correction coding technique was used in the experiment. In this paper, we only use the different number of pilots based on traditional receiving methods. The BiGRU network was constructed, trained, and evaluated in TensorFlow 2.6.2. The model used the cross-entropy loss as the loss function. The Adam optimizer optimized the BiGRU network (learning rate set to 0.005). The dataset comprised 250 symbols and 36,500 data sets, divided into training data (80%) and test data (20%). Other related parameters of the experimental platform are introduced in Table 1.  
signals before and after the proposed algorithm is equalized with the original transmission signal and is also estimated [24]. It is often used to assess the similarity of two distributions. The smaller value indicates how close the two distributions are. KL divergence is calculated as  
\[D(P||Q)=\sum P(x)\log_{2}\frac{P(x)}{Q(x)}, \tag{19}\]  
where \(P(x)\) is the actual transmitted signal and \(Q(x)\) is the different received signals. The KI divergence results are shown in Figure 5b, and the KL divergence value between the signal recovered based on the BiLSTM and BiGRU algorithms and the originally transmitted signal is close, which is about \(1/5\) of the KL divergence between the directly received signal and the original signal. The proposed deep learning network can reduce the KL value, that is, the signal distribution after the algorithm recovery is closer to the distribution of the original transmitted signal, which further shows that both the BiLSTM and BiGRU algorithms have a strong ability to equalize visible-light channel interference. In addition, the KL values of the two algorithms are similar, indicating that BiGRU can achieve a similar equilibrium effect to BiLSTM. Figure 6 shows the recovery of the received OFDM signal by BiGRU and BiLSTM. With an SNR of 16 dB, the waveform of the signals processed by the two receivers is compared to the OFDM transmitted signal. Both algorithms can better recover the original transmitted signal data. We calculated the mean squared error between the recovered signal and the original transmitted signal based on these two algorithms, with a result of 0.000142 for BiGRU and 0.000179 for BiLSTM. The results verify that both algorithms can learn the feature pair of the channel and the received signal equalization by learning the long-term dependent information. Table 2 presents a comprehensive comparison of the practical intricacies involved in processing OFDM signals using both the BiGRU and BiLSTM architectures. All computations were conducted on the same computing platform. A salient observation is that the nonlinear equalizer is initially trained offline and, during the equalization process, the parameters within the nonlinear equalizer remain fixed.