# BigGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication
## 4 Results and Discussion
This strategic reduction in the parameter count contributes to a reduction in testing time and computational overhead. Figure 5: (**a**) Comparison of the originally received waveform with the waveform after processing by the direct receiver and the ideal received waveform. (**b**) Comparison of KL divergence of the received and transmitted signals by different receivers. Based on the experimental findings, both algorithms exhibit comparable channel learning and equilibrium capabilities. However, it is noteworthy that the parameter count of the BiGRU model is 19.69% lower than that of the BiLSTM counterpart. BiGRU also necessitates 22.34% fewer multiplicative operations per symbol in comparison to BiLSTM. This discrepancy in computational requirements can be attributed to the streamlined architecture of the GRU within BiGRU when compared to the LSTM gate in BiLSTM. These empirical results lend further credence to the structural analysis of the two algorithms, as expounded upon in Section 3. Furthermore, this study delves into a comparison of the BER across three distinct reception methods, elucidated in Section 2, under varying SNRs. Of particular significance is the fact that all DL-based reception methods employ pilots set to a value of 0. The outcomes are visually depicted in Figure 7. Notably, Figure 7 highlights the elevated BER exhibited by the conventional receiver relying on LS and MMSE channel estimations due to the absence of pilot signals. The insufficiency of channel information and the corresponding incapacity to discern channel characteristics account for this higher BER. While Figure 7 displays a marginal enhancement in the BER of LS and MMSE when compared to Figure 7, the impact of augmenting the pilot count from 16 to 32 does not manifest a significant improvement. This pattern implies a threshold beyond which the inclusion of additional pilots ceases to appreciably enhance system performance. Strikingly, Figure 7 underscores the exceptional performance of the MMSE estimator-based receiver following the addition of 32 pilots. This outcome underscores the pivotal role of pilot signals in enhancing the performance of this receiver type, which now exhibits parity with conventional DL-based receivers. In the overarching analysis, Figure 7 delineates the performance hierarchy: traditional LS channel estimation-based receivers exhibit the poorest performance, while direct receivers surpass typical DL-based and traditional multi-pilot receivers. Especially within the signal-to-noise ratio range of 0 dB to 20 dB, the direct receiver consistently outperforms its counterparts, especially beyond an SNR of 8 dB. It is worth highlighting that the BER of the MMSE receiver, with 32 pilots, closely approximates that of a conventional DL-based \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Number of Parameters** & **Number of Multiplications** \\ \hline BiGRU & 4,967,232 & 81,323,520 \\ BiLSTM & 6,185,928 & 104,576,576 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Complexity comparison of different receiver algorithms. Figure 6: Waveform comparison plots comparing the BiGRU-based receiver, BiLSTM-based receiver, ideal reception, and transmit sequence. (**a**) shows the results of the training set and (**b**) shows the results of the test set. Both methods can recover the original transmit signal better. receiver devoid of pilots, yet it remains higher than the BER of the pilot-less direct receiver presented in this study. Noteworthy is the observation that although contemporary research predominantly centers on DL-based receivers, network models employed for post-_FFT_ data processing exhibit commendable error rate performance even without pilot signals, even surpassing the performance of the MMSE estimator with 32 pilots. However, the proposed receiver method attains the most favorable error rate performance. The deep learning-based receiver, even without pilot signals, consistently demonstrates the optimal error rate performance.