Machine Learning based Digital Beamforming for Line-of-Sight optimization in Satcom on the Move Technology

Singh, Arushi Jitendra; Jayakumar, M

doi:10.1109/iceca49313.2020.9297645

Cited by 2 publications

(1 citation statement)

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“…Furthermore, AOA estimation with the lowest possible complexity in intricate environments has been achieved in [35], where a data-driven approach employs a MUSIC algorithm in several regression models. In [36], a linear regression model is used in combination with ordinary least squares to accurately predict DOAs of the incoming signals. The support vector regression (SVR) model proposed in [37] proves its ability to faster obtain a precise result of DOAs of signals that do not exist in the learning phase through generalization.…”

Section: B Literature Review Of Machine Learning Based Beamformingmentioning

confidence: 99%

A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

et al. 2022

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The key objective of this paper is to explore the recent state-of-the-art artificial intelligence (AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented beamforming studies are thoroughly investigated in order to correctly comprehend and profitably interpret the AI contribution in the beamforming performance. Starting from a brief overview of beamforming, including adaptive beamforming algorithms and direction of arrival (DOA) estimation methods, our analysis probes further into the main machine learning (ML) classes, the basic neural network (NN) topologies, and the most efficient deep learning (DL) schemes. Subsequently, and based on the prior aspects, the paper explores several concepts regarding the optimal use of ML and NNs either as standalone beamforming and DOA estimation techniques or in combination with other implementations, such as ultrasound imaging, massive multipleinput multiple-output structures, and intelligent reflecting surfaces. Finally, particular attention is drawn on the realization of beamforming or DOA estimation setups via DL topologies. The survey closes with various important conclusions along with an interesting discussion on potential future aspects and promising research challenges.

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Section: B Literature Review Of Machine Learning Based Beamformingmentioning

confidence: 99%

A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

et al. 2022

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Design and Analysis of Digital Beamforming for SATCOM on the Move Based on Specific Geographical Area

Neeraj

Dinesh

Kumar

et al. 2022

Lecture Notes in Networks and Systems

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Machine Learning based Digital Beamforming for Line-of-Sight optimization in Satcom on the Move Technology

Cited by 2 publications

References 8 publications

A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

Design and Analysis of Digital Beamforming for SATCOM on the Move Based on Specific Geographical Area

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