Applications of Machine Learning and Deep Learning in Antenna Design, Optimization, and Selection: A Review

Sarker, Nayan; Podder, Prajoy; Mondal, M. Rubaiyat Hossain; Shafin, Sakib Shahriar; Kamruzzaman, Joarder

doi:10.1109/access.2023.3317371

Cited by 23 publications

(1 citation statement)

References 133 publications

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“…Google based on the LZ77 concept in 2011 developed Snappy, previously known as Zippy, in C++. Its primary goal is to maximize compression speed, resulting in the highest possible speed [14][15][16][17]. Benchmark tests for snappy utilize a Core i7 with a single core in 64-bit mode, achieving compression ratios 20-100% lower than gzip.…”

Section: Snappymentioning

confidence: 99%

Comparative analysis of MapReduce and Apache Tez Performance in Multinode clusters with data compression

Sifat Ibtisum,

S M Atikur Rahman,

S. M. Saokat Hossain

2023

World J. Adv. Res. Rev.

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This article conducts a thorough comparative analysis of Apache Tez and MapReduce in the context of big data processing. It focuses on key performance metrics, scalability, and ease of use. The analysis begins with an overview of the architectural distinctions between the two frameworks, emphasizing their fundamental design principles. A detailed performance evaluation follows, considering factors such as execution time, resource utilization, and throughput across diverse workloads. The study explores scalability by examining how Apache Tez and MapReduce respond to increasing data volumes and computational demands. Cluster size effects, resource allocation strategies, and adaptability to dynamic workloads are scrutinized. Additionally, the article evaluates the frameworks' ease of use for developers and administrators, incorporating aspects like programming model simplicity, debugging capabilities, and system configurability. User experiences are gathered through surveys and practical use cases. The conclusions drawn from this analysis offer valuable insights for organizations and practitioners seeking suitable distributed computing frameworks. By addressing both performance and user experience, the article aims to provide a comprehensive perspective on the strengths and weaknesses of Apache Tez and MapReduce, assisting decision-makers in making informed choices for their big data processing requirements.

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Section: Snappymentioning

confidence: 99%

Comparative analysis of MapReduce and Apache Tez Performance in Multinode clusters with data compression

Sifat Ibtisum,

S M Atikur Rahman,

S. M. Saokat Hossain

2023

World J. Adv. Res. Rev.

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Inverse design of a novel multiport power divider based on hybrid neural network

Sun,

Zhu,

et al. 2024

Concurrency and Computation

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SummaryIn this study, we propose an inverse design approach based on a neural network for a novel multiport power divider (MP‐PD) with complex geometry. The inverse design approach is obtaining geometry from the desired physical performance to address the challenge of conventional methods. We develop a hybrid neural network model for this inverse design. The backbone architecture incorporates a bidirectional long short‐term memory module, a multihead self‐attention module, and convolutional modules. This hybrid neural network is employed to capture the feature of physical performance and learn the relationship between the geometric structure of the proposed MP‐PD and its corresponding physical performance. Consider the design of the power divider as an end‐to‐end methodology that directly maps design requirements to optimal geometric parameters. The neural network transfers the designed process into multiple‐input‐multiple‐output. We adopt the network model to successfully predict 20 geometric parameters of MP‐PDs for two distinct operating frequencies. The two operating frequencies are those utilized in real engineering applications, which are 3.5 GHz in the 5G band and 2.45 GHz in the trackside communication band. The predicted MP‐PD improves the return loss and bandwidth by 8.05 dB and 0.25 GHz, respectively, over the desired performance. The experiments and comparisons demonstrate the effectiveness and accuracy of our inverse design approach. The efficiency and flexibility of design are also significantly improved by the hybrid neural network model.

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Design of a Multiband Antenna Using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT Applications

Bessant

2024

Int J Communication

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The rapid expansion of Internet of Things (IoT) applications presents unprecedented challenges for antenna design, demanding solutions that are versatile, efficient, and capable of operating across multiple frequency bands. This paper addresses these challenges through the development of a design of a multiband antenna using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT applications (DMA‐ACWGAN‐IoT). Here, the Auxiliary Classifier Wasserstein Generative Adversarial Network (ACWGAN) is employed to generate synthetic data representing electromagnetic field distributions and antenna characteristics across various frequencies. The proposed metamaterial‐based Multiple‐Input Multiple‐Output (MIMO) antenna contains four discrete elements, each provided by a micro strip feed. The structures overall width and length are 60 and 52 mm. The metamaterial is printed on a patch and dispersed from the fields with the most effective coupling. The proposed structures strong impedance bandwidth works at 8.3 GHz, 10.8 GHz, 12.3 GHz, 13.7 GHz, 16.1 GHz, and 18.1 GHz, fabricating the proposed antenna prototype using a substrate made of FR‐4 material. The proposed DMA‐ACWGAN‐IoT design provides 6.5 dB maximum gain and 25.40%, 21.60%, and 20.05% higher efficiency compared to existing dual band antenna design with resonance frequency prediction under machine learning models (DBA‐PRF‐ML), machine learning verification depending on a distinctive SWB multiple slotted four‐port high isolated MIMO antenna loaded with metasurface for the applications of IoT (SWB‐MIMO‐IOT‐ML), and dual‐band miniaturized composite right–left‐handed transmission line ZOR antenna along machine learning method for microwave communication (DB‐ZORA‐MC‐ML).

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Applications of Machine Learning and Deep Learning in Antenna Design, Optimization, and Selection: A Review

Cited by 23 publications

References 133 publications

Comparative analysis of MapReduce and Apache Tez Performance in Multinode clusters with data compression

Comparative analysis of MapReduce and Apache Tez Performance in Multinode clusters with data compression

Inverse design of a novel multiport power divider based on hybrid neural network

Design of a Multiband Antenna Using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT Applications

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