Machine Learning Algorithms in Smart Antenna and Arrays for Internet of Things Applications

Javali, Abhishek; Sahoo, Anindita; Gupta, Jagrati

doi:10.1109/icais50930.2021.9395928

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…Artificial intelligence methods have attracted great attention in recent years [59], and they are widely used in all walks of life, opening up new ways for intelligent optimization methods. Especially in mobile communication scenarios, the artificial intelligence method adaptively adjusts the weight coefficients of each element in the antenna array on the basis of the dynamic perception of the electromagnetic environment in space, and then changes the pattern shape, polarization, and other performance parameters of the smart antenna while in orbit.…”

Section: Sparse Area Array Optimization Based On Artificial Intellige...mentioning

confidence: 99%

Optimization Design for Sparse Planar Array in Satellite Communications

He¹,

Wang²

2023

Electronics

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The antenna is one of the key components of satellite communication load. To address the evolving requirements of future satellite communication systems, the sparse planar array has become an important device for transmitting and receiving electromagnetic waves in emerging antenna systems. The advantages of this technology include low cost, low system complexity, and robust anti-interference ability, which have attracted widespread attention within the industry. In this paper, we investigate an optimization design of sparse planar arrays in satellite communication scenarios. Firstly, we introduce the mathematical foundation of the array antennas and establish the optimization design model of the sparse planar array. Secondly, we analyze and compare the impact of different array layout methods on the sparse planar array antenna pattern, and then introduce the latest design trend of array material design. Thirdly, we review some classical optimization methods for optimizing sparse planar arrays and the recent research advancements in promising and novel methods. Lastly, on the basis of the present research status, we propose three future research directions and two critical challenges for optimal design of sparse planar arrays in satellite communication scenarios, which can facilitate the development and realization of array technology under future B5G and 6G wireless networks.

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Section: Sparse Area Array Optimization Based On Artificial Intellige...mentioning

confidence: 99%

Optimization Design for Sparse Planar Array in Satellite Communications

He¹,

Wang²

2023

Electronics

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“…The main reasons for using the unsupervised learning are the model building and the dimensionality reduction [97]. As it is written in [98], the unsupervised learning is the key component in the 6G systems.…”

Section: Neural Network Classification According To the Learning Typementioning

confidence: 99%

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

et al. 2022

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The usage of techniques of the artificial neural networks (ANNs) in the field of microwave devices has recently increased. The advantages of ANNs in comparison with traditional full-wave methods are that the prediction speed when the traditional time-consuming iterative calculations are not required and also the complex mathematical model of the microwave device is no longer needed. Therefore, the design of microwave device could be repeated many times in real time. However, methods of artificial neural networks still lag behind traditional full-wave methods in terms of accuracy. The prediction accuracy depends on the structure of the selected neural network and also on the obtained dataset for the training of the network. Therefore, the paper presents a systematic review of the implementation of ANNs in the field of the design and analysis of microwave devices. The guidelines for the systematic literature review and the systematic mapping research procedure, as well as the Preferred Report Items for Systematic Reviews and Meta-Analysis statements (PRISMA) are used to conduct literature search and report the results. The goal of the paper is to summarize the application areas of usage of ANNs in the field of microwave devices, the type and structure of the used artificial neural networks, the type and size of the dataset, the interpolation and the augmentation of the training dataset, the training algorithm and training errors and also to discuss the future perspectives of the usage of ANNs in the field of microwave devices.

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“…Jiang and Schotten [141] studied a deep RNN-based approach including the longshort-term memory (LSTM) and the gated recurrent unit to estimate the channel condition. ML has been utilized in different antenna-related Internet of Things (IoT) applications [142]. The single-input multiple-output (SIMO) system is a great example of a modern IoT application where ANN enabled significant improvements in learning the modulationdemodulation schemes for multipath channels [143].…”

Section: Miscellaneous Applicationsmentioning

confidence: 99%

“…[ [133][134][135][136][137][138][139][140][141][142][143][144][145]154,157,162,165] ANN Optimization of dual-band antenna with desired return loss, Significant improvements in learning the modulation demodulation schemes for multipath channels in IoT applications.…”

Section: Remote Object Detection and Recognition Cnn Dcnnmentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

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Machine Learning Algorithms in Smart Antenna and Arrays for Internet of Things Applications

Cited by 4 publications

References 34 publications

Optimization Design for Sparse Planar Array in Satellite Communications

Optimization Design for Sparse Planar Array in Satellite Communications

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

Application of Machine Learning in Electromagnetics: Mini-Review

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