Deep Diffusion Models for Robust Channel Estimation
- URL: http://arxiv.org/abs/2111.08177v1
- Date: Tue, 16 Nov 2021 01:32:11 GMT
- Title: Deep Diffusion Models for Robust Channel Estimation
- Authors: Marius Arvinte and Jonathan I Tamir
- Abstract summary: We introduce a novel approach for multiple-input multiple-output (MIMO) channel estimation using deep diffusion models.
Our method uses a deep neural network that is trained to estimate the gradient of the log-likelihood of wireless channels at any point in high-dimensional space.
- Score: 1.7259824817932292
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Channel estimation is a critical task in digital communications that greatly
impacts end-to-end system performance. In this work, we introduce a novel
approach for multiple-input multiple-output (MIMO) channel estimation using
deep diffusion models. Our method uses a deep neural network that is trained to
estimate the gradient of the log-likelihood of wireless channels at any point
in high-dimensional space, and leverages this model to solve channel estimation
via posterior sampling. We train a deep diffusion model on channel realizations
from the CDL-D model for two antenna spacings and show that the approach leads
to competitive in- and out-of-distribution performance when compared to
generative adversarial network (GAN) and compressed sensing (CS) methods. When
tested on CDL-C channels which are never seen during training or fine-tuned on,
our approach leads to end-to-end coded performance gains of up to $3$ dB
compared to CS methods and losses of only $0.5$ dB compared to ideal channel
knowledge. To encourage open and reproducible research, our source code is
available at https://github.com/utcsilab/diffusion-channels .
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