Fast and robust adaptive beamforming method based on complex‐valued RBF neural network

Li, Yuqing; Yang, Xiaopeng; Liu, Feifeng

doi:10.1049/joe.2019.0275

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…It will show how the main challenge of implementation in classic algorithms for the defined criterion problem will be solved according to its results and simulations. Several neural architectures of varying complexity multi-layer perceptrons (MLP) [15,16], convolutional neural networks (CNN) [17,18] and recurrent neural networks (RNN) [19] have been applied to this end. A significant advantage of these methods lies in their low feedforward computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Deep unfolding model‐based for hybrid robust wide band adaptive beamforming

Janani,

Fatemi Mofrad

2024

IET Microwaves Antenna & Prop

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The design of arrays capable of receiving wideband signals differs from arrays that can only receive narrowband signals. These arrays must be able to receive signals with an instant bandwidth of several GHz across the entire operating frequency, such as High‐Resolution Radars or Terahertz in 6G communication systems. In these arrays, using a time delay line structure leads to an increase in beamformer coefficients, resulting in high computational complexity. This poses a challenge for beamforming in wideband systems. Additionally, classic Wideband beamformers face other factors, such as poor performance in the presence of input direction of arrival error, array calibration error, and requiring too many snapshots to reach the steady state of the beamformer. Therefore, the robustness of wideband adaptive beamforming using deep unfolding model‐based technique is focused on, which has not been discussed before. The advent of deep unfolding, an innovative technique, amalgamates iterative optimization approaches with elements of neural networks. The aim is to deftly maneuver through various tasks across disciplines such as machine learning, signal and image processing, and telecommunication systems. Also, the network training method is done to become more robust against the mentioned factors. In the proposed structure, the constraints of the previous methods have been evaluated. It is observed to have better performance compared to other classic algorithms. Also, with the investigations of the proposed method with other conventional deep learning methods, it was observed that in some cases the proposed structure performance is equal to the conventional deep learning method and sometimes better.

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Section: Introductionmentioning

confidence: 99%

“…Most of the studies are focused on improving the performance of narrowband beamforming [15,16,20], and the design of adaptive wideband beamforming in the time domain is in the scope of deep learning [21].…”

Section: Introductionmentioning

confidence: 99%