Distributed Robust Beamforming Based on Low-Rank and Cross-Correlation Techniques: Design and Analysis

Ruan, Hang; Lamare, Rodrigo C. de

doi:10.1109/tsp.2019.2954519

Cited by 39 publications

(36 citation statements)

References 82 publications

(161 reference statements)

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“…The IRWLS procedure presented in this section is performed to obtain the optimal solution by solving the primal problem, whereas the conventional QP optimization problem mentioned in Section 3 obtains the solution by solving the dual problem. The IRWLS procedure for the dual problem can be constructed by forcing the solution to be expanded into a linear combination of whitened vectors, such as (17). The number of unknowns for dual problems is 2N , where N is the number of inequality constraints established by sampling the beampattern, whereas the number of unknowns for the primal problem is 2M , where M is the length of the weighted vector.…”

Section: Irwls Proceduresmentioning

confidence: 99%

“…However, such methods fail when the SNR is low or the dimension of signal plus interference subspace is high [11]. In addition to the above two kinds of approaches, other robust beamforming methods include the Bayesian beamformer [15] [16], the lowrank cross-correlation-based technique [17], the convex optimization-based method [18] [19], and the semidefinite programming-based approach [20].…”

Section: Introductionmentioning

confidence: 99%

“…Then, the weighted vectors w can be obtained in accordance with(17). Each complex weighted coefficient can be formed as ( ) iM = .…”

mentioning

confidence: 99%

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Notice of Violation of IEEE Publication Principles: Robust Adaptive Beamforming Using Support Vector Machines

et al. 2020

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A robust adaptive beamformer based on the support vector machines is proposed to solve the rapid degradation of the performance of minimum variance distortionless response beamformer when the steering vector is mismatched. The proposed beamformer assumes that the angle error range in the steering vector can be estimated; the array only responds within this range, and the array response outside the angle error range is considered as the abnormal response. The proposed method is expressed in the form of support vector regression, and Vapnik's  -insensitive quadratic loss function which can improve the robustness of beamformer is used as a regular term to punish the abnormal response. The assumption that the array only responds within a certain angle range reflects the sparsity of support vector regression and improves the ability of the algorithm against steering vector mismatch because the energy conservation theorem holds that the response is large. The regular term ensures that a deep null exists in the direction of interference, the interference can be effectively suppressed. The iterative reweighted least square procedure can converge to the real solution of the SVM regression problem and is easy to calculate; thus, it is used to solve the algorithm proposed in this study. The effectiveness of the algorithm is verified through computer simulations.

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Section: Irwls Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Notice of Violation of IEEE Publication Principles: Robust Adaptive Beamforming Using Support Vector Machines

et al. 2020

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“…For the wireless sensors, it is envisioned that a new design paradigm is needed to support large numbers of heterogeneous sensing devices with diverse requirements and unique traffic characteristics. Comparing to the sensors in traditional IoT networks, those deployed in extreme environments need to operate in harsh (sometimes hazardous) conditions and are prone to wear and tear, and cannot be easily replaced, posing major challenges in designing resilient networks for robust communications [40]. Our work assumes a centralised control mechanism where sensors are connected to a fusion node via wireless links.…”

Section: Case Study a The Cps Model Designmentioning

confidence: 99%

Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes

et al. 2022

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This paper investigates the challenging fault prediction problem in process industries that adopt autonomous and intelligent cyber-physical systems (CPS), which is in line with the emerging developments of industrial internet of things (IIoT) and Industry 4.0. Particularly, we developed an end-to-end deep learning approach based on a large volume of real-time sensory data collected from a chemical plant equipped with wireless sensors. Firstly, a novel recursive architecture with multi-lookback inputs is proposed to perform autoregression on imbalanced time-series data as a preliminary prediction. In this process, a novel learning algorithm named recursive gradient descent (RGD) is developed for the proposed architecture to reduce cumulative prediction uncertainties. Subsequently, a classification model based on temporal convolutions over multiple channels with decay effect is proposed to perform multi-class classification for fault root cause identification and localization. The overall network is named the cumulative uncertainty reduction network (CURNet), for its superior capacity in reducing prediction uncertainties accumulated over multiple prediction steps. Performance evaluations show that CURNet is able to achieve superior performance especially in terms of fault prediction recall and fault type classification accuracy, compared to the existing techniques.

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“…In addition, algorithms based on different gradients have been derived to update the beamforming weights, reducing computational cost. In the same way, cross-correlation methods have been applied to relay systems, where the correlation is explored between the system's output and received data from the relays at the destination [50]. The proposed distributed beamforming with low-rank and cross-correlation has the objective to maximize the output SINR under total relay power constraint.…”

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confidence: 99%

Precoding and Resource Allocation for Cell-Free Massive Mimo Systems

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Distributed Robust Beamforming Based on Low-Rank and Cross-Correlation Techniques: Design and Analysis

Cited by 39 publications

References 82 publications

Notice of Violation of IEEE Publication Principles: Robust Adaptive Beamforming Using Support Vector Machines

Notice of Violation of IEEE Publication Principles: Robust Adaptive Beamforming Using Support Vector Machines

Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes

Precoding and Resource Allocation for Cell-Free Massive Mimo Systems

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