Improved block sparse Bayesian learning based DOA estimation for massive MIMO systems

Liu, Zhongyan; Liu, Yang; Long, Xudong; Zhang, Yinghui; Qiu, Tianshuang

doi:10.1016/j.aeue.2023.154666

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…In recent years, the research on 2D DOA estimation has gradually increased, and scholars have proposed many array geometries for 2D estimation, such as circular arrays, rectangular arrays and cross arrays. Compared with these 2D arrays, L-shaped arrays are simpler in structure, easier to implement, and have better estimation performance, which has attracted much attention [21].…”

Section: Introductionmentioning

confidence: 99%

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

Jin,

Wang,

Hou

et al. 2023

Sensors

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To reduce the influence of gain-phase errors and improve the performance of direction-of-arrival (DOA) estimation, a robust sparse Bayesian two-dimensional (2D) DOA estimation method with gain-phase errors is proposed for L-shaped sensor arrays. The proposed method introduces an auxiliary angle to transform the 2D DOA estimation problem into two 1D angle estimation problems. A sparse representation model with gain-phase errors is constructed using the diagonal element vector of the cross-correlation covariance matrix of two submatrices of the L-shaped sensor array. The expectation maximization algorithm derives unknown parameter expression, which is used for iterative operations to obtain off-grid and signal precision. Using these parameters, a new spatial spectral function is constructed to estimate the auxiliary angle. The obtained auxiliary angle is substituted into a sparse representation model with gain and phase errors, and then the sparse Bayesian learning method is used to estimate the elevation angle of the incident signal. Finally, according to the relationship of the three angles, the azimuth angle can be estimated. The simulation results show that the proposed method can effectively realize the automatic matching of the azimuth and elevation angles of the incident signal, and improves the accuracy of DOA estimation and angular resolution.

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Section: Introductionmentioning

confidence: 99%

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

Jin,

Wang,

Hou

et al. 2023

Sensors

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An optimized deep learning model for a highly accurate DOA and channel estimation for massive MIMO systems

Pabbati,

Joshi

2024

Int J Communication

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SummaryMassive multiple‐input multiple‐output (MA‐MIMO) has been hailed as an auspicious technology for the future generation of wireless communications because it can considerably increase the capacity of the communication network. However, using the maximum likelihood (ML) direction‐of‐arrival (DOA) estimate method is severely constrained in actual systems because of the computationally expensive multi‐dimensional searching procedure. This paper proposes a novel approach to estimate DOA and channels by incorporating deep learning into the MA‐MIMO system. Here, a deep belief network (DBN) is used to learn both the spatial structures in the angle domain and the statistics of the wireless channel through both online and offline learning procedures. Also, a bald eagle search (BES) Optimization is used along with DBN to attain high precision through optimal training. The proposed model can estimate the channel based on the predicted DOA and the complex gain. According to numerical results, the suggested method performs significantly better than state‐of‐the‐art methods, particularly in tough conditions like low signal‐to‐noise ratio (SNR) and a finite number of snapshots. The proposed DBN‐BES technique accomplishes less root mean square error (RMSE) as 0.01 for SNR of 5 dB in elevation calculation and 0.02 for SNR of 5 dB in azimuth calculation. Also, the proposed algorithm greatly reduces computational complexity.

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Neural network-based DOA estimation for distributed sources in massive MIMO systems

Wang,

Liu,

et al. 2024

AEU - International Journal of Electronics and Communications

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Improved block sparse Bayesian learning based DOA estimation for massive MIMO systems

Cited by 5 publications

References 46 publications

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

An optimized deep learning model for a highly accurate DOA and channel estimation for massive MIMO systems

Neural network-based DOA estimation for distributed sources in massive MIMO systems

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