Enhancing Channel Estimation in Quantized Systems with a Generative Prior

arXiv:2405.03542v1 Announce Type: cross
Abstract: Channel estimation in quantized systems is challenging, particularly in low-resolution systems. In this work, we propose to leverage a Gaussian mixture model (GMM) as generative prior, capturing the channel distribution of the propagation environment, to enhance a classical estimation technique based on the expectation-maximization (EM) algorithm for one-bit quantization. Thereby, a maximum a posteriori (MAP) estimate of the most responsible mixture component is inferred for a quantized received signal, which is subsequently utilized in the …

abstract arxiv cs.it cs.lg distribution eess.sp environment expectation-maximization generative low math.it prior propagation resolution systems type work