Accurate channel estimation of on-grid partially coherent compressive phase retrieval for mmWave massive MIMO systems

Baranidharan Varadharajan; Surendar Maruthu

doi:10.1515/freq-2024-0250

Article

Accurate channel estimation of on-grid partially coherent compressive phase retrieval for mmWave massive MIMO systems

Baranidharan Varadharajan and Surendar Maruthu

Published/Copyright: January 10, 2025

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From the journal Frequenz Volume 79 Issue 5-6

Abstract

Channel estimation of sparse signal is a challenging task for millimeter-wave massive multi-input multi-output systems. Due to the hardware imperfections and use of large carrier frequency oscillator in hybrid precoding architectures leads to random phase drifts in the received pilots. Channel estimation with random phase drifts exploits partial coherence, where pilots share phase distortion within a frame but differ across different frames. To achieve the reliable and efficient communication, the support vector initialization needs to be optimized effectively. In this article, the singular value decomposition-based on-grid partially coherent compressive phase retrieval (SVD-PC-CPR) algorithm is proposed. This algorithm optimizes the initial support vector by finding the best dimensional subspace of the k sparsity elements by forming the singular vectors. The channel vectors are estimated by applying the gradient descent method through iterative refinements. This algorithm utilizes SVD for identifying the dominant paths from the phases of the measurements by selecting basis vectors. Based on the simulation results, the proposed algorithm achieves better normalized mean squared error, a multi-user sum-rate gain of 19 bits/s/Hz, and a 48 % improvement in estimation accuracy over the conventional PC-CPR algorithm at 20 dB signal-to-noise ratio.

Keywords: mmWave communication; channel estimation; hybrid precoding; compressive phase retrieval; partial coherence; support vector initialization

Corresponding author: Baranidharan Varadharajan, Research Scholar, Department of Electronics and Communication Engineering, National Institute of Technology Puducherry, Karaikal 609609, India; and Assistant Professor, Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathy 638401, India. E-mail: svbaranidhar@gmail.com

Acknowledgments

The authors would like to extend their Special thanks to National Institute of Technology Puducherry for providing the 5G & Beyond – Advanced Design and Test Research Laboratory facilities and environment conducive to conducting our research.

Research ethics: Not applicable.
Informed consent: Not applicable.
Author contributions: Authors (VBD) and (MS) have accepted responsibility for the entire content of this manuscript and approved its submission.
Use of Large Language Models, AI and Machine Learning Tools: None declared.
Conflict of interest: The author states no conflict of interest.
Research funding: None declared.
Data availability: Not applicable.

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Received: 2024-08-02

Accepted: 2024-12-20

Published Online: 2025-01-10

Published in Print: 2025-06-26

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Articles in the same Issue

https://doi.org/10.1515/freq-2024-0250

Keywords for this article

mmWave communication; channel estimation; hybrid precoding; compressive phase retrieval; partial coherence; support vector initialization