BiLSTM based Precise Estimation of Rayleigh Fading Channel

Accurate wireless channel estimation is essential for optimizing communication systems, particularly in 5G and future wireless networks. This study explores a deep learning-based approach to estimate the received power in a wireless channel, using distance, fading effects, and noise characteristics as input features. The deep neural network is designed with multiple hidden layers, incorporating batch normalization, dropout regularization, and L2 weight decay to enhance generalization and stability. The model is trained on synthetically generated channel data, simulating path loss, Rayleigh fading, and additive noise to represent realistic propagation conditions. The proposed deep learning model is trained using Mean Squared Error (MSE) loss and optimized with the Adam optimizer, along with a learning rate scheduler to improve convergence. Evaluation shows that the model achieves a low Mean Absolute Error (MAE), indicating strong predictive accuracy. The simulated and predicted power curves demonstrate minimal deviation, confirming the model's capability to generalize well across different channel conditions. Results suggest that this deep learning-based channel estimation approach effectively captures complex propagation characteristics, making it suitable for real-time applications in wireless communication systems. The model can aid in beamforming, resource allocation, and interference management, contributing to enhanced network efficiency.