robust beamformer
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Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 370
Author(s):  
Ruijie Guo ◽  
Chunling Fu ◽  
Yong Jin ◽  
Zhentao Hu ◽  
Lin Zhou

This paper considers the physical layer security (PLS) of a simultaneous wireless information and power transfer (SWIPT) relay communication system composed of a legitimate source–destination pair and some eavesdroppers. Supposing a disturbance of channel status information (CSI) between relay and eavesdroppers in a bounded ellipse, we intend to design a robust beamformer to maximum security rate in the worst case on the constraints of relay energy consumption. To handle this non-convex optimization problem, we introduce a slack variable to transform the original problem into two sub-problems firstly, then an algorithm employing a semidefinite relaxation (SDR) technique and S-procedure is proposed to tackle above two sub-problems. Although our study was conducted in the scene of a direct link among source, destination, and eavesdroppers that is non-existing, we demonstrate that our conclusions can be easily extended to the scene for which a direct link among source, destination and eavesdroppers exist. Numerical simulation results compared with the benchmark scheme are provided to prove the effectiveness and superior performance of our algorithm.


Frequenz ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Diksha Thakur ◽  
Vikas Baghel ◽  
Salman Raju Talluri

Abstract The Capon beamformer has excellent resolution and interference suppression capability but due to various attributes of practical environment such as inaccurate and/or insufficient information about the source, transmission medium and antenna array its performance deteriorates. To enhance its performance various efforts have been devoted and one effective method is presented here. In this paper, a novel and efficient robust Capon beamformer is devised which is based on proximal gradient method (PGRCB) and the robustness is achieved through remodeling the optimization problem of standard Capon beamformer (SCB). In the proposed PGRCB, the proximal gradient method is used to formulate a new optimization problem in order to obtain the optimum weights of the robust beamformer. The proposed method can achieve better performance as compared to some recent methods in the literature and its effectiveness is verified by the simulation results.


Author(s):  
N Samadzadehaghdam ◽  
B MakkiAbadi ◽  
E Eqlimi ◽  
F Mohagheghian ◽  
H Khajehpoor ◽  
...  

Background: Brain source imaging based on electroencephalogram (EEG) data aims to recover the neuron populations’ activity producing the scalp potentials. This procedure is known as the EEG inverse problem. Recently, beamformers have gained a lot of consideration in the EEG inverse problem.Objective: Beamformers lack acceptable performance in the case of correlated brain sources. These sources happen when some regions of the brain have simultaneous or correlated activities such as auditory stimulation or moving left and right extremities of the body at the same time. In this paper, we have developed a multichannel beamformer robust to correlated sources. Material and Methods: We have looked at the problem of brain source imaging and beamforming from a blind source separation point of view. We focused on the spatially constraint independent component analysis (scICA) algorithm, which generally benefits from the pre-known partial information of mixing matrix, and modified the steps of the algorithm in a way that makes it more robust to correlated sources. We called the modified scICA algorithm Multichannel ICA based EEG Beamformer (MIEB).Results: We evaluated the proposed algorithm on simulated EEG data and compared its performance quantitatively with three algorithms: scICA, linearly-constrained minimum-variance (LCMV) and Dual-Core beamformers; it is considered that the latter is specially designed to reconstruct correlated sources.Conclusion:The MIEB algorithm has much better performance in terms of normalized mean squared error in recovering the correlated/uncorrelated sources both in noise free and noisy synthetic EEG signals. Therefore, it could be used as a robust beamformer in recovering correlated brain sources. 


2015 ◽  
Vol 51 (17) ◽  
pp. 1302-1304 ◽  
Author(s):  
Qiang Li ◽  
Wei Wang ◽  
Dingjie Xu ◽  
Xianpeng Wang ◽  
Zifa Han

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