# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 6 Results
### Hybrid Model Performances  
The combination of BiLSTM and LSTM was experimented with in the first step. The input data are first passed through a BiLSTM layer and then through an LSTM layer. The BER results for the BiLSTM-LSTM combination are shown in Figure 13, with a value of \(2.08\times 10^{-4}\) at an SNR of 30 dB. However, when the layer order is reversed, the BER increases to \(2.64\times 10^{-4}\). This indicates that IRS channel data demodulation is more accurate with the BiLSTM-LSTM combination. A similar trend is observed in Figure 14, which shows the SER performance for both BiLSTM-LSTM and LSTM-BiLSTM layers. Next, we consider the LSTM-GRU and GRU-LSTM layer combinations for IRS communication. As seen in Figure 13, the LSTM-GRU layer combination achieves BER of \(2.38\times 10^{-4}\), while the GRU-LSTM combination has a BER of \(2.86\times 10^{-4}\) at 30 dB SNR. This indicates better performance for the LSTM-GRU combination compared to GRU-LSTM, as LSTM can extract primary features more accurately, and GRU can be applied for classification. This combination demonstrates better accuracy compared to LSTM-BiLSTM. The SER for the LSTM and GRU-  
GRU combination is shown in Figure 14. The final considered combination is between BiLSTM and GRU. Both combinations yield excellent results for BER and SER. At 30 dB SNR, the GRU-BiLSTM combination has a BER of \(9.72\times 10^{-5}\), while the BiLSTM-GRU combination achieves a BER of \(9.45\times 10^{-5}\). Among all the combinations, BiLSTM-GRU demonstrates the best BER performance. Since BiLSTM processes data in both forward and backward directions, its feature extraction capabilities are enhanced. Additionally, the GRU layer helps extract important features, leading to high classification accuracy. Thus, the BiLSTM-GRU-based model achieves more accurate results compared to other combinations.  
We also compared the performance of the proposed RNN architecture with a traditional wireless communication system without IRS reflection. For this experiment, we adopted three hybrid models, BiLSTM-GRU, GRU-BiLSTM, and LSTM-BiLSTM, as these models perform well in IRS reflection channels. As shown in Figure 15, all models demonstrate good performance at certain SNRs, but the overall performance is not satisfactory.  
Figure 14: SER of LSTM, BiLSTM, and GRU hybrid models.  
Figure 13: BER of LSTM, BiLSTM, and GRU hybrid models.  
Specifically, in the high SNR range, the system without IRS fails to achieve low BER compared to the IRS-based system. This result highlights the superior BER performance of IRS-based systems.  
BiLSTM-GRU model's performance was also evaluated with other studies in the literature. Figure 16 compares the performance with other similar studies. The proposed BiLSTM-GRU model can perform well as compared to TCN [34] and traditional demodulation techniques. However, it has a similar performance to Demod-CNN [35] as CNNs are very powerful in classifying data.  
Figure 16: Comparison with different deep learning models with the proposed BiLSTM-GRU model.  
Figure 15: Comparison of the wireless communication channel performance with and without IRS using RNN hybrid models.