Application of neural networks in estimating second-order characteristics of $$\kappa $$ κ – $$\mu $$ μ shadowed fading channels

This study introduces a neural network-based approach to estimate the level crossing rate (LCR) and average fade duration (AFD) for wireless fading channels characterized by a $$\kappa $$ κ – $$\mu $$ μ shadowed model. The feed-forward architecture of the neural network is optimized for modeling the complex dynamics inherent in wireless communications, handling the non-linear relationships and stochastic nature of fading signals effectively. Extensive simulations were conducted using a dataset of one million samples, emphasizing the robustness and predictive accuracy of the model. The network was trained using a binary cross-entropy loss function and the RMSprop optimizer, ensuring efficient learning and generalization capabilities. Results demonstrate the network’s ability to closely approximate the statistical distributions of signal fading, offering valuable insights into the behavior of fading channels, which are critical for optimizing mobile communication systems.