Can I read Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO on EtoBox?

Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO by Yu, Wentao; He, Hengtao; Yu, Xianghao; Song, Shenghui; Zhang, Jun; Murch, Ross; Letaief, Khaled B. is a scholarly article available to read on EtoBox.

What is Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO about?

Holographic MIMO (HMIMO) is being increasingly recognized as a key enabling technology for 6G wireless systems through the deployment of an extremely large number of antennas within a compact space to fully exploit the potentials of the electromagnetic (EM) channel. Nevertheless, the benefits of HMIMO systems cannot be fully unleashed without an efficient means to estimate the high-dimensional channel, whose distribution becomes increasingly complicated due to the accessibility of the near-field region. In this paper, we address the fundamental challenge of designing a low-complexity Bayes-optimal channel estimator in near-field HMIMO systems operating in unknown EM environments. The core idea is to estimate the HMIMO channels solely based on the Stein's score function of the received pilot signals and an estimated noise level, without relying on priors or supervision that is not feasible in practical deployment. A neural network is trained with the unsupervised denoising score matching objective to learn the parameterized score function. Meanwhile, a principal component analysis (PCA)-based algorithm is proposed to estimate the noise level leveraging the low-rank near-field spatia

Author

Yu, Wentao; He, Hengtao; Yu, Xianghao; Song, Shenghui; Zhang, Jun; Murch, Ross; Letaief, Khaled B.

Published

2023

Language

EN