Score-Based Generative Models for Robust Channel Estimation
- Resource Type
- Conference
- Authors
- Arvinte, Marius; Tamir, Jonathan I.
- Source
- 2022 IEEE Wireless Communications and Networking Conference (WCNC) Wireless Communications and Networking Conference (WCNC), 2022 IEEE. :453-458 Apr, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
General Topics for Engineers
Signal Processing and Analysis
Wireless communication
Training
Wireless sensor networks
System performance
Neural networks
Channel estimation
Performance gain
Estimation
Score-Based
Deep Learning
Robustness
- Language
- ISSN
- 1558-2612
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 score-based generative models. Our method uses a deep neural network that is trained to estimate the gradient of the log-prior of wireless channels at any point in high-dimensional space, and leverages this model to solve channel estimation via posterior sampling. We train a score-based 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-D channels, the approach leads to a gain of at least 5 dB in channel estimation error compared to GAN methods in-distribution at λ/2 antenna spacing. When tested on CDL-C channels which are never seen during training or fine-tuned on, the 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.