连续波泥脉冲数据传输方法基于连续梯度频率键调和卷积神经网络解调
Yingzhong Zhu1, Zhenhua Xia2, Yue Yang3
1School of Electronic Information and Electrical Engineering, Yangtze University, JingZhou, 434022, China.
Scientific reports
|July 9, 2025
概括
一种新的连续分级频率调节 (CGFK) 调制与卷积神经网络 (CNN) 调节有效地减少测量在钻井 (MWD) 系统中的符号间干扰 (ISI). 与传统技术相比,这种先进的泥脉冲数据传输方法可以提高数据速率和比特错误率.
科学领域:
- 石油工程是石油工程中的一个.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 钻井过程中的测量 (MWD) 对于复杂的井口导航和地下评估至关重要.
- 由于电机惯性,MWD中的高速数据传输面临符号间干扰 (ISI),限制了像频率转换键 (FSK) 和相位转换键 (PSK) 这样的传统方法.
研究的目的:
- 引入和评估一种新的连续分级频率调制 (CGFK) 调制与卷积神经网络 (CNN) 调制相结合,用于连续的泥脉冲数据传输.
- 为了解决和减轻高速MWD数据传输中引擎惯性引起的ISI.
主要方法:
- 开发了一种CGFK调制技术,在符号周期内控制波形频率坡道.
- 使用卷积神经网络 (CNN) 来调节CGFK信号,利用频率变化的速度作为关键特征.
- 通过模拟和物理测试将CGFK-CNN性能与传统方法 (匹配波器,SVM,LSTM) 进行比较.
主要成果:
- 与其他机器学习模型和传统方法相比,CNN在识别CGFK信号方面表现优异.
- 使用CNN解调的CGFK有效避免或减少因电机惯性引起的ISI.
- 实现了有利的信息传输速率和比特错误率 (BER),超过了传统FSK和PSK.
结论:
- CGFK调制与CNN调制相结合,为MWD系统中的高速,可靠的数据传输提供了强大的解决方案.
- 这种创新方法通过克服传统调制技术的局限性,提高了MWD在具有挑战性的钻井环境中的能力.
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