Channel estimation is a crucial task in wireless communication systems to accurately estimate the wireless channel's characteristics. Traditional methods for channel estimation often rely on mathematical models and assumptions, which may not capture the complex and dynamic nature of real-world channels. In recent years, deep learning techniques have demonstrated significant potential in diverse domains, including wireless communications. In this paper, a deep learning-driven framework for channel estimation is developed. This approach uses deep learning techniques with the Least Square (LS), or with Element-Wise-Minimum Mean Squared Error (EW-MMSE) methods. The selection of these methods highlights their simplicity, effectiveness, and compatibility with deep learning models. The profound learning capacity of Deep Neural Networks (DNNs) is used to understand the relationship between detected signals and the corresponding channel parameters. By formulating the channel estimation problem as a regression task, a DNN was trained to reduce the Mean Square Error (MSE) between the estimated and actual channel parameters. The simulation results of this work provide convincing evidence that the proposed approach is effective. Comparing the proposed approach with classic methods reveals its superior performance in terms of robustness to noise and computational efficiency. It achieves lower complexity than the exact Minimum Mean Square Error (MMSE).
 
Special Issue of AEIC 2024 (Electrical and System & Computer Engineering  Session)