scholarly journals 2D DOA Estimation Algorithm for Massive MIMO using Propagator Method

2017 ◽  
Vol 10 (7) ◽  
pp. 13-24
Author(s):  
Basit Iqbal ◽  
Rahim Umar
Keyword(s):  
Massive Mimo ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Propagator Method ◽  
2D Doa Estimation

scholarly journals Compressed Sensing PARALIND Decomposition-Based Coherent Angle Estimation for Uniform Rectangular Array

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Wu Wei ◽  
Xu Le ◽  
Zhang Xiaofei ◽  
Li Jianfeng
Keyword(s):  
Compressed Sensing ◽  
Data Storage ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Rectangular Array ◽  
Propagator Method ◽  
Coherent Sources ◽  
2D Doa Estimation ◽  
Uniform Rectangular Array

In this paper, the topic of coherent two-dimensional direction of arrival (2D-DOA) estimation is investigated. Our study jointly utilizes the compressed sensing (CS) technique and the parallel profiles with linear dependencies (PARALIND) model and presents a 2D-DOA estimation algorithm for coherent sources with the uniform rectangular array. Compared to the traditional PARALIND decomposition, the proposed algorithm owns lower computational complexity and smaller data storage capacity due to the process of compression. Besides, the proposed algorithm can obtain autopaired azimuth angles and elevation angles and can achieve the same estimation performance as the traditional PARALIND, which outperforms some familiar algorithms presented for coherent sources such as the forward backward spatial smoothing-estimating signal parameters via rotational invariance techniques (FBSS-ESPRIT) and forward backward spatial smoothing-propagator method (FBSS-PM). Extensive simulations are provided to validate the effectiveness of the proposed CS-PARALIND algorithm.

A Higher-Order Propagator Method for 2D-DOA Estimation in Massive MIMO Systems

2020 ◽  
Vol 24 (3) ◽  
pp. 543-547 ◽  
Author(s):  
Tanveer Ahmed ◽  
Xiaofei Zhang ◽  
Wajih Ul Hassan
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Doa Estimation ◽  
Higher Order ◽  
Propagator Method ◽  
2D Doa Estimation

2D DOA Estimation Using Sparse Representation of Higher-Order Power of Covariance Matrix

2014 ◽  
Vol 998-999 ◽  
pp. 779-783
Author(s):  
Zheng Luo ◽  
Fei Yu ◽  
Lin Wu ◽  
Yuan Liu
Keyword(s):  
Covariance Matrix ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Higher Order ◽  
Eigenvalue Decomposition ◽  
Coherent Signals ◽  
Sparse Decomposition ◽  
Sparse Signal Representation ◽  
2D Doa Estimation ◽  
Source Signals

A novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm utilizing a sparse signal representation of higher-order power of covariance matrix is proposed. Through applying the higher-order power of covariance matrix to construct a new sparse decomposition vector, this algorithm avoids the estimation of incident signal number and eigenvalue decomposition. And the hierarchical granularity-dictionary is studied, which forms the over-complete dictionary adaptively in the light of source signals’ distribution. Compared with MUSIC and L1-SVD, this algorithm not only provides a better 2D DOA performance but also possesses the capability of coherent signals estimation. Theoretical analysis and simulation results demonstrate the validity and robust of the proposed algorithm.

scholarly journals 2D DOA Estimation with a Two-Parallel Array Consisting of Two Uniform Large-Spacing Linear Arrays

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Sheng Liu ◽  
Jing Zhao ◽  
Yu Zhang
Keyword(s):  
Mutual Coupling ◽  
Elevation Angle ◽  
Doa Estimation ◽  
Matrix Product ◽  
Linear Arrays ◽  
Parallel Array ◽  
Propagator Method ◽  
The Matrix ◽  
2D Doa Estimation ◽  
Estimation Precision

In this paper, an improved propagator method (PM) is proposed by using a two-parallel array consisting of two uniform large-spacing linear arrays. Because of the increase of element spacing, the mutual coupling between two sensors can be reduced. Firstly, two matrices containing elevation angle information are obtained by PM. Then, by performing EVD of the product of the two matrices, the elevation angles of incident signals can be estimated without direction ambiguity. At last, the matrix product is used again to obtain the estimations of azimuth angles. Compared with the existed PM algorithms based on conventional uniform two-parallel linear array, the proposed PM algorithm based on the large-spacing linear arrays has higher estimation precision. Many simulation experiments are presented to verify the effect of proposed scheme in reducing the mutual coupling and improving estimation precision.

scholarly journals Codebook-Aided DOA Estimation Algorithms for Massive MIMO System

Electronics ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 26 ◽  
Author(s):  
Shufeng Li ◽  
Hongda Wu ◽  
Libiao Jin
Keyword(s):  
Massive Mimo ◽  
Large Scale ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Computational Cost ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Frame Structure ◽  
Green Communication ◽  
Estimation Algorithms

The conventional direction of arrival (DOA) estimation algorithm is not effective with the tremendous complexity due to the large-scale array antennas in a massive multiple-input multiple-output (MIMO) system. A new frame structure for downlink transmission is presented. Then, codebook-aided (C-aided) algorithms are proposed based on this frame structure that can fully exploit the priori information under channel codebook feedback mechanism. An oriented angle range is scoped through the codebook feedback, which is drastically beneficial to reduce computational burden for DOA estimation in massive MIMO systemss. Compared with traditional DOA estimation algorithms, our proposed C-aided algorithms are computationally efficient and meet the demand of future green communication. Simulations show the estimation effectiveness of C-aided algorithms and advantage for decrement of computational cost.

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