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

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.

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

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1121
Author(s):  
Prateek Saurabh Srivastav ◽  
Lan Chen ◽  
Arfan Haider Wahla
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Optimization Problem ◽  
Mimo Systems ◽  
Mimo System ◽  
Low Rank ◽  
Potential Candidate ◽  
Alternating Direction ◽  
The Individual ◽  
Joint Channel

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.

scholarly journals Millimeter-Wave Channel Estimation using Alternating Direction Method of Multipliers

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.

scholarly journals Label Distribution Learning with Label Correlations via Low-Rank Approximation

2019 ◽  
Author(s):  
Tingting Ren ◽  
Xiuyi Jia ◽  
Weiwei Li ◽  
Shu Zhao
Keyword(s):  
Correlation Matrix ◽  
Learning Algorithm ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Real World Data ◽  
Label Correlations ◽  
Rank Approximation ◽  
Label Distribution Learning ◽  
Label Distribution ◽  
Label Correlation

Label distribution learning (LDL) can be viewed as the generalization of multi-label learning. This novel paradigm focuses on the relative importance of different labels to a particular instance. Most previous LDL methods either ignore the correlation among labels, or only exploit the label correlations in a global way. In this paper, we utilize both the global and local relevance among labels to provide more information for training model and propose a novel label distribution learning algorithm. In particular, a label correlation matrix based on low-rank approximation is applied to capture the global label correlations. In addition, the label correlation among local samples are adopted to modify the label correlation matrix. The experimental results on real-world data sets show that the proposed algorithm outperforms state-of-the-art LDL methods.

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

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.

scholarly journals A Scalable Kernel-Based Semisupervised Metric Learning Algorithm with Out-of-Sample Generalization Ability

2008 ◽  
Vol 20 (11) ◽  
pp. 2839-2861 ◽  
Author(s):  
Dit-Yan Yeung ◽  
Hong Chang ◽  
Guang Dai
Keyword(s):  
Learning Algorithm ◽  
Metric Learning ◽  
Low Rank ◽  
Data Sets ◽  
Low Rank Approximation ◽  
Real World Data ◽  
Data Set ◽  
Out Of Sample ◽  
Rank Approximation ◽  
Kernel Approach

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.

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