DOI: 10.5281/zenodo.17067880
title: 📡RadPat-50K: A Large-Scale Benchmark for Antenna Array Radiation Patterns

description: 📡 RadPat-50K: A Large-Scale Benchmark for Antenna Array Radiation Patterns

RadPat-50K is a large-scale synthetic dataset of 50,000 radiation patterns generated for Uniform Linear Arrays (ULA).Each sample includes polar plots and rectangular plots of the array factor, along with metadata for design and performance parameters.

📊 Dataset Highlights



Size: 50,000 radiation patterns

Array Elements (N): 4, 8, 12, 16, 24, 32, 48, 64

Element Spacing (λ): 0.25, 0.5, 0.75, 1.0

Steering Angles (°): –60, –45, –30, –15, 0, 15, 30, 45, 60

Weighting Schemes: 



Uniform

Binomial

Cosine

Kaiser

Hamming

Hann

Blackman

Exponential



Variations (for diversity):



⚡ Amplitude noise

📐 Phase noise

🎯 Steering jitter




🔬 Applications

This dataset is well-suited for research in machine learning, deep learning, and signal processing, including:



📑 Antenna pattern classification

🎛️ Beamforming analysis

🚨 Grating lobe detection

🏗️ Data-driven array design





🧾 Metadata per Record

Each radiation pattern entry includes:



Antenna parameters: number of elements, element spacing, steering angle, weighting scheme

Noise parameters: amplitude noise, phase noise, steering jitter


🧾 Performance Metrics



📡 Directivity

📏 Half-Power Beamwidth (HPBW)

 📐 Main Lobe Angle

 



🤖 Benchmark Potential

RadPat-50K provides a standardized benchmark for:







Training and evaluating classification & regression models

Developing robust antenna array designs under noise conditions

Advancing AI-driven RF and antenna research
description: 📂 Dataset Description

The RadPat-50K dataset is organized into three primary files for ease of experimentation:



📁 Rect_50K.zip → Contains 50,000 rectangular plot images of antenna array radiation patterns.

📁 Polar_50K.zip → Contains 50,000 polar plot images of antenna array radiation patterns.

📑 Metadata_50K.csv → A structured metadata file providing detailed information for each sample.


📊 Metadata Contents

Each record in Metadata_50K.csv includes:



id → Unique identifier for each sample

N → Number of array elements

spacing_wavelengths → Element spacing in wavelengths

weights → Applied weighting scheme

steering_deg_nominal → Nominal steering angle (in degrees)

steering_deg_noisy → Steering angle with noise (in degrees)

amp_noise_std → Standard deviation of amplitude noise

phase_noise_std → Standard deviation of phase noise

grating_lobe → Presence of grating lobe (True / False)

image_rect → File name of the corresponding rectangular plot

image_polar → File name of the corresponding polar plot

D_peak_dBi → Peak directivity (in dBi)

HPBW_deg → Half-Power Beamwidth (in degrees)

main_lobe_angle_deg → Main lobe angle (in degrees)


 ⚡ Quick Experimentation Subset (RadPat-10K)

For rapid prototyping and experimentation, we provide a 10K subset of the full dataset.This subset maintains the same structure, variations, and metadata fields as the full 50K dataset, but with fewer samples for faster loading and reduced storage needs.

To ensure transparency, accessibility, and wider reach, the RadPat-50K dataset is hosted across multiple trusted public repositories:



📊 Kaggle – for seamless integration with notebooks and ML workflows

🤗 Hugging Face Datasets – for direct use in deep learning pipelines

📡 IEEE Dataport – for the research and engineering community

📑 Mendeley Data – for academic visibility and long-term preservation


This multi-platform availability ensures that researchers, engineers, and practitioners can easily access, reproduce, and build upon the dataset.
description: 1. Classification Benchmarks



Task A: Weighting Scheme Classification



Input: Radiation pattern image

Output: Weighting scheme label (uniform, cosine, blackman, etc.)


Task B: Number of Elements Classification



Input: Radiation pattern image

Output: Class label (N = 4, 8, 12, 16, 24, 32, 48, 64)


Task C: Spacing Classification



Input: Radiation pattern image

Output: Class label (d = 0.25λ, 0.5λ, 0.75λ, 1.0λ)


Task D: Joint Classification



Input: Radiation pattern image

Output: Multi-task prediction (N, spacing, weighting, steering angle category).






2. Regression Benchmarks



Task E: Directivity Prediction



Input: Radiation pattern image

Output: Directivity (linear or dB).


Task F: HPBW Prediction



Input: Radiation pattern image

Output: Half Power Beamwidth in degrees.






3. Multi-Label / Structured Prediction



Task G: Parameter Recovery



Input: Radiation pattern image

Output: A set of antenna parameters (N, spacing, weighting scheme, steering angle).






4. Vision-Language Benchmarks (VQA-style)



Task H: Antenna Q&A



Input: (Image + Question)

Example Qs:



"What is the main lobe direction?"

"Which weighting scheme is applied?"

"How many array elements are used?"


Output: Answer (text).


Task I: Captioning



Input: Radiation pattern image

Output: Caption like"16-element array, 0.5λ spacing, uniform weighting, steered to 30° with gain ≈ 12 dB and HPBW ≈ 14°."