Autoregressive-Gaussian Mixture Models: Efficient Generative Modeling of WSS Signals

Abstract: This work addresses the challenge of making generative models suitable for resource-constrained environments like mobile wireless communication systems. We propose a generative model that integrates Autoregressive (AR) parameterization into a Gaussian Mixture Model (GMM) for modeling Wide-Sense Stationary (WSS) processes. By exploiting model-based insights allowing for structural constraints, the approach significantly reduces parameters while maintaining high modeling accuracy. Channel estimation experiments show that the model can outperform standard GMMs and variants using Toeplitz or circulant covariances, particularly with small sample sizes. For larger datasets, it matches the performance of conventional methods while improving computational efficiency and reducing the memory requirements.