Channel Estimation in Low-Resolution Near-Field Massive MIMO Systems
Nguyen, Ly V.; Nguyen, Duy H.N.; Atzeni, Italo; Tolli, Antti; Swindlehurst, A. Lee (2024-08-26)
IEEE
26.08.2024
L. V. Nguyen, D. H. N. Nguyen, I. Atzeni, A. Tölli and A. L. Swindlehurst, "Channel Estimation in Low-Resolution Near-Field Massive MIMO Systems," 2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM), Corvallis, OR, USA, 2024, pp. 1-5, doi: 10.1109/SAM60225.2024.10636448
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202410246437
https://urn.fi/URN:NBN:fi:oulu-202410246437
Tiivistelmä
Abstract
Massive multiple-input-multiple-output (MIMO) is a core technology of current and future wireless networks. However, the very large dimension of a massive antenna array can lead to radical changes in the electromagnetic fields near the array, and the classical far-field channel model is no longer accurate. Instead, the channel should be modeled under the assumption of near-field spherical wavefronts. Furthermore, the very large dimension of the arrays can also result in high power consumption and hardware complexity. A practical solution for this problem is to use low-resolution analog-to-digital converters (ADCs). It is therefore of significance to study the near-field channel estimation problem for MIMO systems implemented with low-resolution ADCs. We propose an efficient on-grid polar-domain channel estimation method which relies on the polar-domain sparsity of the near-field channels. We first reformulate the sparse low-resolution near-field maximum-likelihood channel estimation problem by exploiting an approximation of the cu-mulative distribution function of a normal random variable as a logistic activation function. We then develop an on-grid polar-domain channel estimation method based on the gradient descent approach and the polar-domain sparsity of the near-field channel. Finally, we apply the deep unfolding technique to optimize the performance of the proposed method and illustrate its efficiency via several simulation studies.
Massive multiple-input-multiple-output (MIMO) is a core technology of current and future wireless networks. However, the very large dimension of a massive antenna array can lead to radical changes in the electromagnetic fields near the array, and the classical far-field channel model is no longer accurate. Instead, the channel should be modeled under the assumption of near-field spherical wavefronts. Furthermore, the very large dimension of the arrays can also result in high power consumption and hardware complexity. A practical solution for this problem is to use low-resolution analog-to-digital converters (ADCs). It is therefore of significance to study the near-field channel estimation problem for MIMO systems implemented with low-resolution ADCs. We propose an efficient on-grid polar-domain channel estimation method which relies on the polar-domain sparsity of the near-field channels. We first reformulate the sparse low-resolution near-field maximum-likelihood channel estimation problem by exploiting an approximation of the cu-mulative distribution function of a normal random variable as a logistic activation function. We then develop an on-grid polar-domain channel estimation method based on the gradient descent approach and the polar-domain sparsity of the near-field channel. Finally, we apply the deep unfolding technique to optimize the performance of the proposed method and illustrate its efficiency via several simulation studies.
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