Generating Synthetic Csi Data Using Gans: a Deep Learning-Based Approach

Channel State Information (CSI) is essential in wireless communication systems, but acquiring real-world CSI data is costly, time-intensive, and subject to privacy concerns. To overcome these challenges, we present a Generative Adversarial Network (GAN)-based model designed to generate synthetic CSI data while preserving key statistical properties of real CSI signals. Our approach employs a generator that learns distribution of actual CSI and a discriminator that ensures authenticity of generated data. The effectiveness of our synthetic CSI is evaluated using the Kolmogorov-Smirnov (KS) test, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Experimental results indicate that our model achieves a KS Statistic of 0.1330, an MSE of 0.2251, and an MAE of 0.3752, confirming potential of GANs for CSI data augmentation. This research provides a foundation for future advancements, such as incorporating Wasserstein GANs and feature matching techniques to further enhance data quality.