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

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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Channel Estimation Algorithms and Their Impact on Wideband Millimeter Wave Channel Characteristics

2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC) ◽

10.23919/ursi-at-rasc.2018.8471319 ◽

2018 ◽

Cited By ~ 1

Author(s):

Wei Fan ◽

Yilin Ji ◽

Fengchun Zhang ◽

Gert F. Pedersen

Keyword(s):

Channel Estimation ◽

Millimeter Wave ◽

Estimation Algorithms ◽

Channel Characteristics ◽

Wave Channel

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Research on 5G Millimeter Wave Channel Estimation Algorithm

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset19651 ◽

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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Millimeter Wave Time-Varying Channel Estimation via Exploiting Block-Sparse and Low-Rank Structures

IEEE Access ◽

10.1109/access.2019.2937628 ◽

2019 ◽

Vol 7 ◽

pp. 123355-123366 ◽

Cited By ~ 3

Author(s):

Long Cheng ◽

Guangrong Yue ◽

Daizhong Yu ◽

Yueyue Liang ◽

Shaoqian Li

Keyword(s):

Channel Estimation ◽

Millimeter Wave ◽

Low Rank ◽

Time Varying ◽

Time Varying Channel

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

Entropy ◽

10.3390/e22101121 ◽

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.

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