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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Dataset for Antenna Array Pattern with Sidelobe Level Control Using Deep Learning

Authors: Abdullah, Muhammad Asad;

Dataset for Antenna Array Pattern with Sidelobe Level Control Using Deep Learning

Abstract

This dataset contains simulation results (output data) from various amplitude and phase configurations applied to an 8-element linear antenna array. Each row represents a unique combination of amplitude and phase settings, followed by the resulting array factor (AF) calculated across angles from 0° to 180°. Structure of the CSV File Total Columns: 197 Columns 1–8: Amplitudes (A₁, A₂, …, A₈) Represent the amplitude weights applied to each antenna element. Amplitude values range from 0 to 1 in increments of 0.02. Higher amplitude values indicate greater contribution from the corresponding antenna element to the total radiated signal. An amplitude of 0 means the antenna element is effectively turned "off". Columns 9–16: Phases (ϕ₁, ϕ₂, …, ϕ₈) Represent the phase shifts applied to each antenna element, expressed in radians. Phase values range from −90° to +90° in increments of 2°, converted to radians. Phases determine how each antenna element’s signal is shifted, effectively steering the beam direction. Columns 17–197: Array Factor (AF) Each row contains 181 values representing the computed array factor across angles 0° to 180° (at 1° increments). The AF describes the spatial radiation pattern resulting from the combined amplitude and phase settings of the 8-element array. These values can be further processed (e.g., converted to magnitude or dB scale) for detailed analysis. How to Use the Data To use this dataset effectively and efficiently, follow these three basic steps. Load: Load the CSV file into your preferred analytical environment (e.g., Python, MATLAB, R). Extract: Columns 1–8 for amplitudes | Columns 9–16 for phases | Columns 17–197 for array factor / radiation patterns. Analyze: Evaluate how different amplitude and phase combinations affect the antenna array’s radiation pattern. Perform conversions as needed (e.g., magnitude or decibel scale). Utilize the data as input or output for machine learning and optimization tasks, such as predicting radiation patterns or optimizing array configurations. Potential Applications Antenna Array Design: Explore the influence of amplitude tapering and phase shifts on beamforming and radiation patterns. Machine Learning and Optimization: Serve as a comprehensive training dataset for algorithms designed to optimize antenna element settings to meet desired radiation characteristics. Citation If you find this dataset useful for your research or projects, please cite it appropriately: https://doi.org/10.5281/zenodo.14533762

The amplitudes follow a tapering pattern, often setting middle elements closer to 1 to strengthen the main beam while tapering edge elements. Phases transition from negative to positive values to allow effective beam steering across the broadside. The amplitude and phase ranges are specifically chosen to cover realistic and practical configurations commonly encountered in antenna array design.

Keywords

linear antenna array, Deep learning, Array Factor, Deep Neural Network, AESA, Optimized Radiation Pattern, Sidelobe level, antenna, antenna array, Machine learning, Radiation Pattern, Phase Scanning, Dataset, DNN

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average