Low-Complexity ADMM-Based Algorithm for Robust Multi-Group Multicast Beamforming in Large-Scale Systems

Mohamadi, Niloofar; Dong, Min; Shahbazpanahi, Shahram

doi:10.1109/tsp.2022.3160004

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…Theoretical and experimental data indicate that the matrix obtained in Equation (10) still requires diagonal processing to achieve normal noise levels, with the loading amount being the average power of the noise. The specific expression is as follows (11) Among them, is the estimated value of noise power, which can be equivalently replaced by 10 times the minimum eigenvalue of the data covariance matrix.…”

Section: The Proposed Beamforming Methodsmentioning

confidence: 99%

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A Novel Adaptive Beamforming Scheme for Array Signal Data Processing

Shi

Yan

Hou

et al. 2023

Preprint

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When the desired signal data exists in the array received data or the steering vector has a mismatch problem, the current traditional adaptive beamformers will suffer from the effect of the desired signal cancellation phenomenon, resulting in a sharp decline in performance. To address the occurrence of desired signal cancellation, an improved matrix projection-based efficient beamforming method is proposed. Firstly, based on spatial partitioning technology, a significant projection matrix for interference-plus-noise space (INS) is constructed. Secondly, using the constructed key projection matrix, the sample data covariance matrix is projected into the INS to achieve the goal of suppressing the desired signal data information. Finally, the weight data vector is calculated by Capon beamformer. The proposed algorithm does not require iterative search for the optimal solution, which has the advantage of a small amount of calculation. Simulation experiments have verified that the proposed method has significant advantages in suppressing the desired data signals. Especially when the desired data signal has a large power, the signal-to-interference-plus-noise ratio (SINR) of the proposed algorithm is better than that of the compared algorithms under the conditions of random directionality errors or local scattering errors between the desired signal and interference.

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Section: The Proposed Beamforming Methodsmentioning

confidence: 99%

“…Therefore, how to improve the robustness of adaptive beamforming algorithms in non-ideal scenarios is a major research direction [10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Beamforming Scheme for Array Signal Data Processing

Shi

Yan

Hou

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…where S 0 = B • S. This is a nonconvex function and cannot be solved directly. Therefore, we adopt the alternating direction method of multipliers (ADMM) [19] to solve the optimization problem. First, an auxiliary variable U is introduced to transform the retinex energy functional into a convex optimization problem.…”

Section: Proposed Model and Optimizationmentioning

confidence: 99%

Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

Qiu

Niu

et al. 2022

Symmetry

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Images in real surface defect detection scenes often suffer from uneven illumination. Retinex-based image enhancement methods can effectively eliminate the interference caused by uneven illumination and improve the visual quality of such images. However, these methods suffer from the loss of defect-discriminative information and a high computational burden. To address the above issues, we propose a joint-prior-based uneven illumination enhancement (JPUIE) method. Specifically, a semi-coupled retinex model is first constructed to accurately and effectively eliminate uneven illumination. Furthermore, a multiscale Gaussian-difference-based background prior is proposed to reweight the data consistency term, thereby avoiding the loss of defect information in the enhanced image. Last, by using the powerful nonlinear fitting ability of deep neural networks, a deep denoised prior is proposed to replace existing physics priors, effectively reducing the time consumption. Various experiments are carried out on public and private datasets, which are used to compare the defect images and enhanced results in a symmetric way. The experimental results demonstrate that our method is more conducive to downstream visual inspection tasks than other methods.

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“…Typical solutions to distributed optimization problems, which have been widely investigated recently, can be categorized into three classes in general, namely, the alternating direction method of multipliers (ADMM) [3], distributed primaldual methods [4], and distributed subgradient methods (DSG). ADMM is an algorithm that solves convex optimization problems by dividing them into smaller subproblems.…”

Section: Introductionmentioning

confidence: 99%

Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization

Xia¹,

Du²,

Ren³

2022

Preprint

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The distributed subgradient method (DSG) is a widely discussed algorithm to cope with large-scale distributed optimization problems in the arising machine learning applications. Most exisiting works on DSG focus on ideal communication between the cooperative agents such that the shared information between agents is exact and perfect. This assumption, however, could lead to potential privacy concerns and is not feasible when the wireless transmission links are not of good quality. To overcome the challenge, a common approach is to quantize the data locally before transmission, which avoids exposure of raw data and significantly reduces the size of data. Compared with perfect data, quantization poses fundamental challenges on loss of data accuracy, which further impacts the convergence of the algorithms. To settle the problem, we propose a generalized distributed subgradient method with random quantization, which can be intepreted as a two time-scale stochastic approximation method. We provide comprehensive results on the convergence of the algorithm and derive upper bounds on the convergence rates in terms of the quantization bit, stepsizes and the number of network agents. Our results extend the existing results, where only special cases are considered and general conclusions for the convergence rates are missing. Finally, numerical simulations are conducted on linear regression problems to support our theoretical results.

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Low-Complexity ADMM-Based Algorithm for Robust Multi-Group Multicast Beamforming in Large-Scale Systems

Cited by 9 publications

References 39 publications

A Novel Adaptive Beamforming Scheme for Array Signal Data Processing

A Novel Adaptive Beamforming Scheme for Array Signal Data Processing

Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization

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