MIMO Channel Estimation using Score-Based Generative Models
- URL: http://arxiv.org/abs/2204.07122v1
- Date: Thu, 14 Apr 2022 17:23:58 GMT
- Title: MIMO Channel Estimation using Score-Based Generative Models
- Authors: Marius Arvinte, Jonathan I Tamir
- Abstract summary: We introduce a novel approach for channel estimation using deep score-based generative models.
These models are trained to estimate the gradient of the log-prior distribution, and can be used to iteratively refine estimates, given observed measurements of a signal.
- Score: 1.6752182911522517
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Channel estimation is a critical task in multiple-input multiple-output
digital communications that has effects on end-to-end system performance. In
this work, we introduce a novel approach for channel estimation using deep
score-based generative models. These models are trained to estimate the
gradient of the log-prior distribution, and can be used to iteratively refine
estimates, given observed measurements of a signal. We introduce a framework
for training score-based generative models for wireless channels, as well as
performing channel estimation using posterior sampling at test time. We derive
theoretical robustness guarantees of channel estimation with posterior sampling
in single-input single-output scenarios, and show that the observations
regarding estimation performance are verified experimentally in MIMO channels.
Our results in simulated clustered delay line channels show competitive
in-distribution performance without error floors in the high signal-to-noise
ratio regime, and robust out-of-distribution performance, outperforming
competing deep learning methods by up to 5 dB in end-to-end communication
performance, while the complexity analysis reveals how model architecture can
efficiently trade performance for estimation latency.
Related papers
- Joint Sparsity Pattern Learning Based Channel Estimation for Massive
MIMO-OTFS Systems [46.42375183269616]
We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) modulation aided systems.
Both our simulation results and analysis demonstrate that the proposed channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes.
arXiv Detail & Related papers (2024-03-06T15:05:39Z) - Diffusion-based Generative Prior for Low-Complexity MIMO Channel
Estimation [12.192048506302015]
This work proposes a novel channel estimator based on diffusion models (DMs)
CNN with positional embedding of the signal-to-noise ratio (SNR) information is designed by learning the channel distribution in the sparse angular domain.
Results exhibit better performance than state-of-the-art channel estimators utilizing generative priors.
arXiv Detail & Related papers (2024-03-06T08:47:31Z) - Joint Channel Estimation and Feedback with Masked Token Transformers in
Massive MIMO Systems [74.52117784544758]
This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix.
The entire encoder-decoder network is utilized for channel compression.
Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
arXiv Detail & Related papers (2023-06-08T06:15:17Z) - Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation [35.62977046569772]
We develop an unsupervised over-the-air (OTA) algorithm that utilizes noisy received pilot measurements to train a deep generative model.
We then formulate channel estimation from a limited number of pilot measurements as an inverse problem.
Our proposed framework has the potential to be trained online using real noisy pilot measurements.
arXiv Detail & Related papers (2022-05-25T02:26:34Z) - Deep Diffusion Models for Robust Channel Estimation [1.7259824817932292]
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.
arXiv Detail & Related papers (2021-11-16T01:32:11Z) - Learning to Perform Downlink Channel Estimation in Massive MIMO Systems [72.76968022465469]
We study downlink (DL) channel estimation in a Massive multiple-input multiple-output (MIMO) system.
A common approach is to use the mean value as the estimate, motivated by channel hardening.
We propose two novel estimation methods.
arXiv Detail & Related papers (2021-09-06T13:42:32Z) - Model-Driven Deep Learning Based Channel Estimation and Feedback for
Millimeter-Wave Massive Hybrid MIMO Systems [61.78590389147475]
This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for millimeter-wave (mmWave) systems.
To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains, we propose to jointly train the phase shift network and the channel estimator as an auto-encoder.
Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.
arXiv Detail & Related papers (2021-04-22T13:34:53Z) - DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator
Search [55.164053971213576]
convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead.
Structured (channel) pruning is usually applied to reduce the model redundancy while preserving the network structure.
Existing structured pruning methods require hand-crafted rules which may lead to tremendous pruning space.
arXiv Detail & Related papers (2020-11-04T07:43:01Z) - Data-Driven Symbol Detection via Model-Based Machine Learning [117.58188185409904]
We review a data-driven framework to symbol detection design which combines machine learning (ML) and model-based algorithms.
In this hybrid approach, well-known channel-model-based algorithms are augmented with ML-based algorithms to remove their channel-model-dependence.
Our results demonstrate that these techniques can yield near-optimal performance of model-based algorithms without knowing the exact channel input-output statistical relationship.
arXiv Detail & Related papers (2020-02-14T06:58:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.