# ant_sim

**Repository Path**: DarylzDD/ant_sim

## Basic Information

- **Project Name**: ant_sim
- **Description**: antenna simulation
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2024-09-18
- **Last Updated**: 2026-02-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Antenna Simulation Baseline Code

## Introduction
This repository contains the baseline code for antenna simulation developed by DarylzDD. The code includes features such as subarray partitioning, multi-beamforming, and multi-beam scanning.

Please note that the code on GitHub might not be the latest version, but it will be regularly updated. The most recent and up-to-date code can be found at: [https://gitee.com/DarylzDD/ant_sim](https://gitee.com/DarylzDD/ant_sim)

## Features


```
ant_sim
   ├── /file                  # 存放输出结果
   ├── /util                  # 通用工具模块, eg: 画图, 日志一类
   ├── /dissertation          # 论文
   │       ├── chapter_3_feature_cnn2d.py             # 第三章: 自编码器提取特征(输出2D,多层CNN), 来自beam_feature_ae_2d_cnn2d.py
   │       ├── chapter_3_feature_cnn2d_cbam.py        # 第三章: 自编码器提取特征(输出2D,多层CNN+注意力), beam_feature_ae_2d_cnn2d_attention.py
   │       ├── chapter_4_multibeam.py                 # 第四章: 多波束, step1.AE提特征(输出2D+多层CNN+注意力)
   │       └── util_*.py                   # 此部分通用工具模块, 详见具体文件
   ├── /beam_trace            # 动态波束赋形: 单波束
   │       ├── beam_feature_ae_1d_b.py                # 提取特征: 自编码器方式: 输出1D(256), 简单binary
   │       ├── beam_feature_ae_1d_cnn2d.py            # 提取特征: 自编码器方式: 输出1D(256), 多层CNN
   │       ├── beam_feature_ae_1d_cnn2d_attention.py  # 提取特征: 自编码器方式: 输出1D(256), 多层CNN+注意力机制
   │       ├── beam_feature_ae_2d_cnn2d.py            # 提取特征: 自编码器方式: 输出2D(16x16), 多层CNN
   │       ├── beam_feature_ae_2d_cnn2d_attention.py  # 提取特征: 自编码器方式: 输出2D(16x16), 多层CNN+注意力机制
   │       ├── beam_track_1d_ae_lstm.py               # 时间序列预测(应用场景跟踪): 自编码器(输出1D) + lstm
   │       ├── beam_track_1d_lstm.py                  # 时间序列预测(应用场景跟踪): 纯lstm预测
   │       ├── beam_track_2d_ae_lstm.py               # 时间序列预测(应用场景跟踪): 自编码器(输出2D) + lstm
   │       └── util_*.py                   # 此部分通用工具模块, 详见具体文件
   ├── .gitignore             # git ignore
   ├── readme.md              # read me
   ├── requirements.txt       # conda依赖
   └── main.py                # 程序入口文件（并没有用）

```

1. Subarray Partitioning: This feature allows for the division of the antenna array into smaller subarrays, enabling parallel processing and improved performance.

2. Multi-Beamforming: The code supports multi-beamforming, which enables the transmission of multiple beams simultaneously. This improves communication efficiency and capacity.

3. Multi-Beam Scanning: This feature enables the scanning of multiple beams over a target area. It allows for better coverage and flexibility in antenna operations.

## Requirements
To run the code successfully, the following dependencies are required:
- Python 3.10
- matploit, numpy, pandas, tensorflow

Recommend using Anaconda or similar tools to manage Python environments

## Installation
1. Clone the repository from GitHub:
   ```
   git clone https://github.com/DarylzDD/ant_sim.git
   ```
2. Install the required dependencies using pip:
   ```
   pip install -r requirements.txt
   ```
3. Run the code in /subarray or /multi-beam:
   ```
   python XXX.py
   ```

## Contribution
Contributions to the project are welcome. To contribute, please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make the necessary changes and commit them.
4. Push your changes to your forked repository.
5. Submit a pull request to the main repository.

## License
This project is licensed under the [MIT License](https://github.com/DarylzDD/ant_sim/blob/main/LICENSE).

## Contact
For any inquiries or questions regarding the code or project, please reach out to DarylzDD at 351430525@qq.com.

## Acknowledgements
We would like to acknowledge the following individuals for their contributions and support:
- Zhang Naibo
- Yansong Cui
- Jianming Huang
- Zilai Wang
- Kun Deng
- Du Yanjun
- ...

Your contributions are greatly appreciated. Thank you!