Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

Li, Shufeng; Liu, Yiming; Sun, Yao; Cai, Yujun

doi:10.1016/j.phycom.2023.102018

Physical Communication

2023

DOI: 10.1016/j.phycom.2023.102018

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Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

Shufeng Li

Yiming Liu

Yao Sun

et al.

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2024

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Cited by 2 publications

References 31 publications

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Gaussian Mixture—Expectation Maximization–Based Learned AMP Network With CNN Deep Residual Network for Millimeter Wave Communication

Philip,

2024

Int J Communication

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In millimeter wave (mmWave) multiple input and multiple output (MIMO) hybrid systems, estimating beamspace channels is complex due to the use of fewer RF chains compared with the number of transceiver antennas. Additionally, beamspace channels exhibit sparsity, making channel estimation a sparse signal recovery problem. This problem can be addressed using iterative algorithms such as orthogonal matching pursuit (OMP), approximate message passing (AMP), and learned AMP (LAMP) enabled by deep neural networks (DNNs). Two methods—GM‐LAMP and GM‐EM‐LAMP—have been proposed in the literature to improve channel estimation accuracy further. The GM‐LAMP algorithm employs a Gaussian mixture (GM)–based shrinkage function within the LAMP network to enhance estimation accuracy. The GM‐EM‐LAMP algorithm incorporates the expectation maximization (EM) algorithm to recover channel information from the beamspace channel vector. However, both methods result in noisy computed channel matrices. This article addresses this challenge by proposing a GM‐EM‐LAMP network integrated with a convolutional neural network deep residual network (CDRN). The proposed method enhances the contraction function and the DNN, modeling channel estimation as a denoising problem using an element‐wise subtraction structure. The spatial properties of the channel matrix are estimated using GM‐EM‐LAMP, and the outputs of GM‐EM‐LAMP are fed into the CDRN network. Compared with existing algorithms such as LS‐CDRN, OMP‐CDRN, AMP‐CDRN, LAMP‐CDRN, and GM‐LAMP‐CDRN, the proposed approach provides superior results in terms of normalized mean squared error (NMSE) and enhanced spectral gains.

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Gaussian Mixture—Expectation Maximization–Based Learned AMP Network With CNN Deep Residual Network for Millimeter Wave Communication

Philip,

2024

Int J Communication

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On some mixtures of the Kies distribution

Zaevski,

Kyurkchiev

2024

Hacettepe Journal of Mathematics and Statistics

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The purpose of this paper is to explore some mixtures, discrete and continuous, based on the Kies distribution. Some conditions for convergence are established. We study the probabilistic properties of these mixtures. Special attention is taken to the so-called Hausdorff saturation. Several models are examined in detail – bimodal, multimodal, and mixtures based on binomial, geometric, exponential, gamma, and beta distributions. We provide some numerical experiments for real-life tasks – one for the Standard and Poor’s 500 financial index and another for unemployment insurance issues. In addition, we check the consistency of the proposed estimator using generated data of different sizes.

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Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

Cited by 2 publications

References 31 publications

Gaussian Mixture—Expectation Maximization–Based Learned AMP Network With CNN Deep Residual Network for Millimeter Wave Communication

Gaussian Mixture—Expectation Maximization–Based Learned AMP Network With CNN Deep Residual Network for Millimeter Wave Communication

On some mixtures of the Kies distribution

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