利用基于BiLSTM和GAN的深度神经网络,对X参数进行自动化功率放大器优化
1Department of Electrical and Electronics Engineering, Dogus University, 34775 Istanbul, Türkiye.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了使用四个顺序深度神经网络 (DNN) 的高性能功率放大器 (PA) 的自动化设计技术. 该方法精确地优化PA配置和性能,优于传统方法.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 设计高性能功率放大器 (PA) 需要复杂的优化.
- 传统的方法往往耗时,缺乏自动化.
研究的目的:
- 为高性能PAs提出一个自动化设计技术.
- 为了提高PA设计和优化的精度和效率.
主要方法:
- 四个深度神经网络 (DNN) 的连续实施:BiLSTM,GAN和回归BiLSTM.
- 使用多目标狮子优化器 (MOALO) 的超参数优化.
- 使用X参数和负载拉动轮用于拓和配置选择.
主要成果:
- 拟议的自动化方法精确地设计和优化PA.
- 与标准的长期短期存储器 (LSTM) 网络相比,实现了更高的精度.
- 成功设计和优化了一个在1.8GHz到2.2GHz之间运行的PA,输出功率为40dBm.
结论:
- 开发的基于DNN的策略为PA设计提供了完全自动化和高度精确的方法.
- 这种技术显著提高了优化功率放大器性能的效率和准确性.
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