On the Performance of Efficient Channel Estimation Strategies for Hybrid Millimeter Wave MIMO System

Millimeter wave (mmWave) relying upon the multiple output multiple input (MIMO) is a new potential candidate for fulfilling the huge emerging bandwidth requirements. Due to the short wavelength and the complicated hardware architecture of mmWave MIMO systems, the conventional estimation strategies based on the individual exploitation of sparsity or low rank properties are no longer efficient and hence more modern and advance estimation strategies are required to recapture the targeted channel matrix. Therefore, in this paper, we proposed a novel channel estimation strategy based on the symmetrical version of alternating direction methods of multipliers (S-ADMM), which exploits the sparsity and low rank property of channel altogether in a symmetrical manner. In S-ADMM, at each iteration, the Lagrange multipliers are updated twice which results symmetrical handling of all of the available variables in optimization problem. To validate the proposed algorithm, numerous computer simulations have been carried out which straightforwardly depicts that the S-ADMM performed well in terms of convergence as compared to other benchmark algorithms and also able to provide global optimal solutions for the strictly convex mmWave joint channel estimation optimization problem.

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Millimeter-Wave Channel Estimation using Alternating Direction Method of Multipliers

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d8597.0210421 ◽

2021 ◽

Vol 10 (4) ◽

pp. 239-242

Author(s):

Aarab Mohamed Nassim ◽

Chakkor Otman

Keyword(s):

Channel Estimation ◽

Millimeter Wave ◽

Mobile Networks ◽

Mimo Systems ◽

Matrix Completion ◽

Mobile Network ◽

Alternating Direction Method ◽

Low Rank ◽

Wave Channel ◽

Alternating Direction

With the explosive growth in demand for mobile data traffic, the contradiction between capacity requirements and spectrum scarcity becomes more and more prominent. The bandwidth is becoming a key issue in 5G mobile networks. However, with the huge bandwidth from 30 GHz to 300 GHz, mmWave communications considered an important part of the 5G mobile network providing multi communication services, where channel state information considers a challenging task for millimeter wave MIMO systems due to the huge number of antennas. Therefore, this paper discusses the channel and signal models of the mmWave, with a novel formulation for mmWave channel estimation inclusive low rank features, that we improved using a developed theory of matrix completion with Alternating Direction Method.

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GAMP-SBL-based channel estimation for millimeter-wave MIMO systems

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-021-00792-w ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Jianfeng Shao ◽

Xianpeng Wang ◽

Xiang Lan ◽

Zhiguang Han ◽

Ting Su

Keyword(s):

Channel Estimation ◽

Millimeter Wave ◽

Mimo Systems ◽

Learning Algorithm ◽

Mimo System ◽

Estimation Algorithm ◽

Mimo Channel ◽

Estimation Methods ◽

Estimation Accuracy ◽

Signal Recovery

AbstractBased on the finite scattering characters of the millimeter-wave multiple-input multiple-output (MIMO) channel, the mmWave channel estimation problem can be considered as a sparse signal recovery problem. However, most traditional channel estimation methods depend on grid search, which may lead to considerable precision loss. To improve the channel estimation accuracy, we propose a high-precision two-stage millimeter-wave MIMO system channel estimation algorithm. Since the traditional expectation–maximization-based sparse Bayesian learning algorithm can be applied to handle this problem, it spends lots of time to calculate the E-step which needs to compute the inversion of a high-dimensional matrix. To avoid the high computation of matrix inversion, we combine damp generalized approximate message passing with the E-step in SBL. We then improve a refined algorithm to handle the dictionary matrix mismatching problem in sparse representation. Numerical simulations show that the estimation time of the proposed algorithm is greatly reduced compared with the traditional SBL algorithm and better estimation performance is obtained at the same time.

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Sparse Bayes Tensor and DOA Tracking Inspired Channel Estimation for V2X Millimeter Wave Massive MIMO System

Sensors ◽

10.3390/s21124021 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4021

Author(s):

Kaihua Luo ◽

Xiaoping Zhou ◽

Bin Wang ◽

Jifeng Huang ◽

Haichao Liu

Keyword(s):

Channel Estimation ◽

Millimeter Wave ◽

Traffic Safety ◽

Massive Mimo ◽

Mimo System ◽

Wireless Channel ◽

Low Rank ◽

Fast Time ◽

Time Varying ◽

Redundant Dictionary

Efficient vehicle-to-everything (V2X) communications improve traffic safety, enable autonomous driving, and help to reduce environmental impacts. To achieve these objectives, accurate channel estimation in highly mobile scenarios becomes necessary. However, in the V2X millimeter-wave massive MIMO system, the high mobility of vehicles leads to the rapid time-varying of the wireless channel and results in the existing static channel estimation algorithms no longer applicable. In this paper, we propose a sparse Bayes tensor and DOA tracking inspired channel estimation for V2X millimeter wave massive MIMO system. Specifically, by exploiting the sparse scattering characteristics of the channel, we transform the channel estimation into a sparse recovery problem. In order to reduce the influence of quantization errors, both the receiving and transmitting angle grids should have super-resolution. We obtain the measurement matrix to increase the resolution of the redundant dictionary. Furthermore, we take the low-rank characteristics of the received signals into consideration rather than singly using the traditional sparse prior. Motivated by the sparse Bayes tensor, a direction of arrival (DOA) tracking method is developed to acquire the DOA at the next moment, which equals the sum of the DOA at the previous moment and the offset. The obtained DOA is expected to provide a significant angle information update for tracking fast time-varying vehicular channels. The proposed approach is evaluated over the different speeds of the vehicle scenarios and compared to the other methods. Simulation results validated the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art researches.

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Precise Channel Estimation Approach for a mmWave MIMO System

Applied Sciences ◽

10.3390/app10124397 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4397 ◽

Cited By ~ 1

Author(s):

Prateek Saurabh Srivastav ◽

Lan Chen ◽

Arfan Haider Wahla

Keyword(s):

Channel Estimation ◽

Mean Squared Error ◽

Mimo Systems ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Low Rank ◽

High Signal ◽

Formidable Challenge ◽

Input Multiple Output ◽

Efficient Recovery

Channel estimation is a formidable challenge in mmWave Multiple Input Multiple Output (MIMO) systems due to the large number of antennas. Therefore, compressed sensing (CS) techniques are used to exploit channel sparsity at mmWave frequencies to calculate fewer dominant paths in mmWave channels. However, conventional CS techniques require a higher training overhead for efficient recovery. In this paper, an efficient extended alternation direction method of multipliers (Ex-ADMM) is proposed for mmWave channel estimation. In the proposed scheme, a joint optimization problem is formulated to exploit low rank and channel sparsity individually in the antenna domain. Moreover, a relaxation factor is introduced which improves the proposed algorithm’s convergence. Simulation experiments illustrate that the proposed algorithm converges at lower Normalized Mean Squared Error (NMSE) with improved spectral efficiency. The proposed algorithm also ameliorates NMSE performance at low, mid and high Signal to Noise (SNR) ranges.

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Downlink channel estimation for millimeter wave communication combining low-rank and sparse structure characteristics

annals of telecommunications - annales des télécommunications ◽

10.1007/s12243-020-00802-2 ◽

2020 ◽

Author(s):

Jin Zhou

Keyword(s):

Channel Estimation ◽

Millimeter Wave ◽

Dictionary Learning ◽

Mimo Systems ◽

Learning Algorithm ◽

Nuclear Norm ◽

Low Rank ◽

Low Rank Approximation ◽

Norm Theory ◽

Rank Approximation

Abstract The acquisition of channel state information (CSI) is essential in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The mmWave channel exhibits sparse scattering characteristics and a meaningful low-rank structure, which can be simultaneously employed to reduce the complexity of channel estimation. Most existing works recover the low-rank structure of channels using nuclear norm theory. However, solving the nuclear norm-based convex problem often leads to a suboptimal solution of the rank minimization problem, thus degrading the accuracy of channel estimation. Previous contributions recover the channel using over-complete dictionary with the assumption that the mmWave channel can be sparsely represented under some dictionary. While over-complete dictionary may increase the computational complexity. To address these problems, we propose a channel estimation framework based on non-convex low-rank approximation and dictionary learning by exploring the joint low-rank and sparse representations of wireless channels. We surrogate the widely used nuclear norm theory with non-convex low-rank approximation method and design a dictionary learning algorithm based on channel feature classification employing deep neural network (DNN). Our simulation results reveal the proposed scheme outperform the conventional dictionary learning algorithm, Bayesian framework algorithm, and compressed sensing-based algorithms.

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Grouping-Based Channel Estimation and Tracking for Millimeter Wave Massive MIMO Systems

Wireless Communications and Mobile Computing ◽

10.1155/2021/2922359 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Rui Yin ◽

Xin Zhou ◽

Wei Qi ◽

Celimuge Wu ◽

Yunlong Cai

Keyword(s):

Computational Complexity ◽

Channel Estimation ◽

Millimeter Wave ◽

Massive Mimo ◽

Mimo Systems ◽

Mimo System ◽

Closed Form Solution ◽

Network Capacity ◽

Base Station ◽

Estimation Accuracy

Although the millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) system can potentially boost the network capacity for future communications, the pilot overhead of the system in practice will greatly increase, which causes a significant decrease in system performance. In this paper, we propose a novel grouping-based channel estimation and tracking approach to reduce the pilot overhead and computational complexity while improving the estimation accuracy. Specifically, we design a low-complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS). With the recovered signal eigenvectors, the celebrated multiple-signal classification (MUSIC) algorithm can be employed to estimate the direction of arrival (DoA) angles and the path amplitude for the user terminals (UTs). To improve the estimation accuracy and accelerate the tracking speed, we develop a closed-form solution for updating the step-size in the proposed iterative algorithm. Furthermore, a grouping method is proposed to reduce the number of sharing pilots in the scenario of multiple UTs to shorten the pilot overhead. The computational complexity of the proposed algorithm is analyzed. Simulation results are provided to verify the effectiveness of the proposed schemes in terms of the estimation accuracy, tracking speed, and overhead reduction.

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GAMP-SBL Based Channel Estimation for Millimeter-Wave MIMO Systems

10.21203/rs.3.rs-637648/v1 ◽

2021 ◽

Author(s):

Jianfeng Shao ◽

Xianpeng Wang ◽

Xiang Lan ◽

Zhiguang Han ◽

Ting Su

Keyword(s):

Channel Estimation ◽

Millimeter Wave ◽

Message Passing ◽

Mimo Systems ◽

Mimo System ◽

Estimation Algorithm ◽

Mimo Channel ◽

Estimation Methods ◽

Estimation Accuracy ◽

Signal Recovery

Abstract Based on the finite scattering characters of the millimeter-wave multiple-input multiple-output (mmWave MIMO) channel, the mmWave channel estimation problem can be considered as a sparse signal recovery problem. However, most traditional channel estimation methods depend on grid search, which may lead to considerable precision loss. To improve the channel estimation accuracy, we propose a high-precision two-stage millimeter-wave MIMO system channel estimation algorithm. Since the traditional expectation-maximization based sparse Bayesian learning (EM-SBL) algorithm can be applied to handle this problem, however, it spends lots of time to calculate the E-step which needs to compute the inversion of a high dimensional matrix. To avoid the high computation of matrix inversion, we combine damp generalized approximate message passing (DGAMP) with the E-step in SBL. We then improve a refined algorithm to handle the dictionary matrix mismatching problem in sparse representation. Numerical simulations show that the estimation time of the proposed algorithm is greatly reduced compared with the traditional SBL algorithm and better estimation performance is obtained at the same time.

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