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 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 A New Algorithm for Positive Semidefinite Matrix Completion

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Fangfang Xu ◽  
Peng Pan
Keyword(s):  
Matrix Completion ◽  
Positive Semidefinite ◽  
Alternating Direction Method ◽  
Low Rank ◽  
System Control ◽  
Positive Semidefinite Matrix ◽  
Linear Least Squares ◽  
Alternating Direction ◽  
Semidefinite Matrix Completion ◽  
Semidefinite Matrix

Positive semidefinite matrix completion (PSDMC) aims to recover positive semidefinite and low-rank matrices from a subset of entries of a matrix. It is widely applicable in many fields, such as statistic analysis and system control. This task can be conducted by solving the nuclear norm regularized linear least squares model with positive semidefinite constraints. We apply the widely used alternating direction method of multipliers to solve the model and get a novel algorithm. The applicability and efficiency of the new algorithm are demonstrated in numerical experiments. Recovery results show that our algorithm is helpful.

scholarly journals Millimeter Wave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures

2018 ◽  
Vol 17 (2) ◽  
pp. 1123-1133 ◽  
Author(s):  
Xingjian Li ◽  
Jun Fang ◽  
Hongbin Li ◽  
Pu Wang
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Low Rank ◽  
Wave Channel

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 Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 49738-49749
Author(s):  
Ting Jiang ◽  
Maozhong Song ◽  
Xuejian Zhao ◽  
Xu Liu
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems

scholarly journals Research on 5G Millimeter Wave Channel Estimation Algorithm

2019 ◽  
pp. 74-77
Author(s):  
Xu Shuang
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Large Scale ◽  
Low Frequency ◽  
Estimation Algorithm ◽  
Simulation Research ◽  
Wave Technology ◽  
Wave Channel ◽  
Mimo Technology

With the explosive growth in the number of communication users and the huge demand for data from users, Limited low-frequency resources have been far from being satisfied by users. The combination of Massive MIMO technology and millimeter-wave technology has brought new hope to users. In this paper, several basic algorithms are placed under the millimeter wave large-scale antenna channel for simulation research.

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