Enhanced Channel Modeling and Generation with Reconstructive Generative Adversarial Network

Advancements in deep learning (DL)-based wireless communications have been hindered by the increasing complexity of channel modeling and the challenge of collecting high-quality wireless channel data. This study proposes a novel machine learning approach, the Reconstructive Generative Adversarial Network (ReGAN), which leverages an AutoEncoder (AE) architecture to enhance data generation efficiency. The method was assessed using a comprehensive dataset of various Clustered Delay Line channel realizations, created using MATLAB. The ReGAN model demonstrated high consistency in channel similarity, setting a new benchmark in channel modeling and generation. This approach has the potential to expedite the development of wireless communication systems by offering more efficient and accurate channel models.