Channel Prediction Using Adaptive Bidirectional GRU for Underwater MIMO Communications

As the Internet of Things (IoT) continues to expand and reshape our world, new vertical application scenarios have emerged, such as underwater communications, leading to increased interest in academia and industries. The multiple-input-multiple-output (MIMO) technology plays a critical role in enhancing channel capacity for underwater acoustic (UWA) communications, where accurate channel prediction is essential for system performance. In this article, we propose a novel efficient channel impulse response (CIR) prediction model for the UWA MIMO communications with a small adaptive bidirectional gated recurrent unit (ABiGRU) network. The proposed model can capture the channel information without additional knowledge of the internal properties of the channel itself. Moreover, it first utilizes preceding short-term CIR data from the channel estimation for online training, and then exploits the trained model for the CIR prediction, which tracks time-varying UWA channels. To verify the effectiveness of the predicted CIRs, we design a scheme combining a space-time block coding (STBC) and minimum mean square error (MMSE) pre-equalization for the UWA MIMO system. Our proposed STBC-MMSE pre-equalization scheme has demonstrated practical feasibility and low-bit-error rate (BER) in numerical simulations. In addition, we evaluate the prediction error performance of the proposed ABiGRU network through comparison with the widely used MMSE algorithm and two common recurrent neural networks (RNNs) predictors, i.e., the gated recurrent unit and long short term memory (LSTM) network. Finally, we conduct realistic in-field UWA MIMO experiments to demonstrate and justify the superiority of the proposed ABiGRU network, which can lay the solid foundation for cost-effective UWA MIMO communications for building promising underwater IoT sensor networks.