基于变压器的高频通信解调
Can Lu1,2, Feng Wang3,4, Rui Zhu1
1School of Electronic Information, Xijing University, 710123, Xi'an, China.
Scientific reports
|January 9, 2026
概括
本研究引入了对短波频道的深度学习解调算法,在低信号噪声比 (SNR) 环境中显著提高了性能. 基于变压器的方法优于传统技术和其他深度学习模型,提高了通信可靠性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 短波频道面临的重大损害,如多路径和色在低信号噪声比 (SNR) 的环境中,增加比特误差率 (BER).
- 传统的解调技术,如最小平方 (LS) 和最小平均平方误差 (MMSE),在这些条件下难以满足通信要求.
研究的目的:
- 为短波频道提出一种基于深度学习的新型解调算法,以克服传统方法的局限性.
- 在具有挑战性的低SNR环境中提高调节精度和通信质量.
主要方法:
- 采用基于变压器的网络的多道深度学习方法,将真实和虚拟的信号部分作为单独的输入.
- 集成一个卷积神经网络 (CNN) 模块,在网络架构内有效地提取本地特征.
主要成果:
- 拟议的深度学习算法在 -10 dB 到 10 dB SNR 范围内显著优于传统的 LS 和 MMSE 方法在调节性能方面.
- 基于变压器的网络与包括CNN,GRU和CNN-RNN模型在内的基准方法相比,实现了1-5dB的一致SNR收益.
- 在低SNR,高频频道条件下,基于变压器的调节接收器的表现优越.
结论:
- 开发的基于变压器的深度学习解调算法为短波通信系统提供了重大进步.
- 这些发现突出了通过先进的深度学习技术提高短波通信的可靠性和稳定性的潜力.
- 这项研究为未来开发在不利通道条件下运行的强大的通信系统提供了宝贵的见解.
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