使用双分辨器CGAN与BiLSTM和变压器重建OFDM信号
Yuhai Li1, Youchen Fan2, Shunhu Hou1
1Graduate School, Space Engineering University, Beijing 101416, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究引入了一种新的双分辨器CGAN模型,用于增强直角频率分割多重复合 (OFDM) 信号重建. 与现有技术相比,该方法改善了信号质量和复杂性管理.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 传统的直角频率分割多重复合 (OFDM) 信号重建方法面临复杂性和低于最佳性能的挑战.
- 使用常规卷积神经网络 (CNN) 从OFDM信号中提取复杂的时间信息是困难的.
研究的目的:
- 为改进OFDM信号重建提出一种新的双歧视条件生成对抗网络 (CGAN) 模型.
- 增强在OFDM信号中的时间和相关信息的提取,以实现更准确的重建.
主要方法:
- 开发了一种双分辨器CGAN模型,集成BiLSTM和变压器网络.
- 第一个分辨器使用BiLSTM从In-phase和Quadrature-phase (IQ) 和Amplitude和Phase (AP) 序列捕获时间细节.
- 第二个区分器结合了视觉转换器 (ViT) 概念,将智商序列视为图像来进行增强的相关性分析.
主要成果:
- 时间序列波形,星座图和BPSK,QPSK和16QAM调制格式的光谱图的高质量重建.
- 与现有的重建方法相比,拟议的算法证明了优越的信号质量.
- 有效地管理计算复杂性,同时实现改进的重建性能.
结论:
- 双分辨器CGAN模型有效地解决了传统OFDM信号重建中的局限性.
- 整合BiLSTM和变压器架构显著提高了模型处理复杂信号数据的能力.
- 这种方法为在对策和信号处理应用中进行先进的通信信号重建提供了有希望的解决方案.
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