Distinguishing Signal from Noise in 5G MIMO Systems Using Generative Adversarial Networks

In recent years, Generative Adversarial Networks (GANs) have emerged as powerful tools for improving signal processing in advanced communication systems, particularly in the context of 5G networks. In this paper, we present a novel approach for distinguishing signal from noise in 5G Multiple Input Multiple Output (MIMO) systems using GANs. Our method leverages the generative capabilities of GANs to produce realistic noise signals and the discriminative power of GANs to accurately identify real signals amidst noise. By training the GAN on a combination of real-world noisy signals and pure noise, our model achieves robust signal detection and classification. We evaluate our approach using synthetic data, demonstrating significant improvements over other techniques such as the autoencoders. Our results highlight the potential of GANs in enhancing the reliability and performance of 5G MIMO communications.