Research on channel estimation based on joint perception and deep enhancement learning in complex communication scenarios

In contemporary wireless communication systems, channel estimation and optimization have become increasingly pivotal with the growing number and complexity of devices. Communication systems frequently encounter multiple challenges, such as multipath propagation, signal fading, and interference, which may result in the degradation of communication quality, a reduction in data transmission rates, and even communication interruptions. Therefore, effective estimation and optimization of channels in complex communication environments are of paramount importance to ensure communication quality and enhance system performance. In this article, we address the intelligent, reflective surface (IRS)-assisted channel estimation problem and propose an intelligent channel estimation model based on the fusion of convolutional neural network (CNN) and gated recurrent unit (GRU) row features, utilizing the reinforcement learning Deep Deterministic Policy Gradient (DDPG) strategy for Channel Reconstruction Prediction and Generation Network (CRPG-Net). The framework initially acquires the received signal by converting the guide-frequency symbols at the transmitter into time-domain sequences to be transmitted, and after propagating through the direct channel and the IRS reflection channel, processes the data at the receiver. Subsequently, the spatial and temporal features in the received signal are extracted using the CRPG-Net model, with the adaptive optimization capability of the model enhanced by deep reinforcement learning. The introduction of reinforcement learning enables the model to continuously optimize decisions in dynamic channel environments, improve the robustness of channel estimation, and quickly adjust the IRS reflection parameters when the channel state changes to adapt to complex communication conditions. Experimental results demonstrate that the framework achieves significant channel estimation accuracy and robustness across several public datasets and real test scenarios, with the channel estimation error markedly smaller than that of traditional least squares (LS) and linear minimum mean square error (LMMSE) methods. This method introduces innovative techniques for channel estimation in intelligent communication systems, playing a crucial role in enhancing communication quality and overall system performance.