Improved channel estimation for underwater wireless optical communication OFDM systems by combining deep learning and machine learning models

Abstract

Recent studies on channel estimation in wireless communication systems have focused on deep learning methods. Our primary contribution is based on the use of DenseNet121 hybrid with Random Forest (RF), Gated Recurrent Units (GRU), Long Short-Term Memory Networks (LSTM), and Recurrent Neural Networks (RNN) to improve the channel estimation and lower the error rate. In order to mitigate inter-symbol interference and map the datasets, this paper introduces M-quadrature amplitude modulation (16-QAM) and orthogonal frequency division multiplexing (OFDM), which is based on quadrature phase shift keying (QPSK). Additionally, the existence or lack of cyclic prefixes forms the basis of our simulation. Additionally, the suggested models are investigated using pilot samples 2, 4, 8, and 64. Labeled OFDM signal samples, where the labels match the signal received after applying OFDM and passing through the medium, are used to train the proposed models. The DenseNet121 functions as a powerful feature extractor to extract intricate spatial information from received signal data. Sequential models like as RNN, LSTM, and GRU are used to model temporal dependencies in the retrieved features. RF is also utilized to exploit non-linear relationships and interactions between features to further increase prediction accuracy and reduce bit error rate (BER). By comparing the models using key metrics like accuracy, bit error rate (BER), and mean squared error (MSE), superior performance is attained based on the DenseNet121_RNN_GRU_RF model. Additionally, the DLMs are assessed against traditional methods like minimal mean square error (MMSE) and least squares (LS). Using the DenseNet121_RNN_GRU_RF model indicates a considerable gain over alternative architectures, with an improvement of 36.3% over DensNet121-RNN-LSTM-RF, according to a comparison of the suggested models without cyclic prefix for OFDM_QPSK. The improvement in percentages of roughly 63.3% over DensNet121-RNN-LSTM, 68.18% over DensNet121-GRU, 72.7% over DensNet121-LSTM, and 86.3% is the improvements of DenseNet121_RNN_GRU_RF over DensNet121-RNN are 86.3 and 72.7%, respectively, over DensNet121-GRU and DensNet121-LSTM. The DenseNet121_RNN_GRU_RF model performs better than the other models when compared to the suggested model with cyclic prefix for OFDM_QPSK. Compared to DenseNet121_RNN_LSTM_RF, the DenseNet121_RNN_GRU_RF model improves BER by about 45%. In contrast, the DenseNet121_RNN_GRU_RF model outperforms DenseNet121_RNN_LSTM by roughly 66.6%. It outperforms DenseNet121_GRU by 71.4%, DenseNet121_LSTM by 80.9%, and DenseNet121_RNN by 90.4%. Additionally, DenseNet121_RNN_GRU_RF shows a significant improvement over LS, requiring a 70% improvement over the LS approach. DenseNet121_RNN_GRU_RF outperforms the Minimum Mean Square Error (MMSE) by roughly 39.5%. Additionally, when using QPSK, higher pilot counts typically translate into lower MSE values. At MSE = \({10}^{-3},\) the improvement of employing 64 pilot bits over 8 pilot bits is approximately 12.1%. utilizing eight pilot bits improves performance by roughly 21.2% compared to utilizing two or four pilot bits. Performance is improved by approximately 18.9% at BER = \({10}^{-4}\) when there are eight pilots instead of four. Furthermore, there is a 13.8% improvement in accuracy from 8 to 64 pilots, indicating that more pilots can further increase accuracy. Finally, BER performance is greatly improved with additional pilots, as evidenced by the noteworthy 35.3% improvement between 4 and 64 pilots. For OFDM-QPSK, employing CP often results in an improvement of roughly 9% over not utilizing CP. Compared to the LS and MMSE models, the DenseNet121_RNN_GRU_RF model provides a significant BER improvement in terms of error rate reduction and computing time of 4.215 s. This suggests that the model's capacity to precisely estimate the channel and reduce bit errors has significantly improved.