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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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基于自适应波形卷积神经网络的OTFS系统的信号检测方法

You Wu1, Mengyao Zhou1

  • 1Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212000, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
概括

这项研究引入了一个适应波形卷积神经网络 (AWCNN) 用于正交时频空间 (OTFS) 信号检测. 与传统的CNN相比,AWCNN可以提高特征提取和融合速度,从而提高OTFS系统的性能.

科学领域:

  • 无线通信无线通信
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 对于正交时频空间 (OTFS) 信号检测的卷积神经网络 (CNN) 在特征提取和处理信号特征方面面临限制.
  • 在CNN中的固定内核与OTFS信号的稀疏性和非静态性作斗争,导致缓慢的融合和高的培训成本.

研究的目的:

  • 提出一个自适应波形卷积神经网络 (AWCNN),用于增强OTFS信号检测.
  • 为了提高特征提取,融合效率和OTFS系统的整体检测性能.

主要方法:

  • 在CNN中用自适应波段卷积层取代固定卷积内核,特别是使用具有可学习参数的Sym4波段内核.
  • 将原来的接收信号和消息传递 (MP) 算法估计作为输入特征集成到AWCNN模型中.
  • 评估了AWCNN模型的性能与标准CNN在融合效率和比特错误率 (BER) 的表现.

主要成果:

  • 与标准的CNN模型相比,AWCNN模型显示出更高的融合效率.
  • 在低信号噪声比率 (SNR) 2dB的情况下,AWCNN实现了与CNN可比的比特误差率 (BER).
  • 拟议的方法有效地运行,不需要飞行员辅助的道状态信息获取.
关键词:
适应波形波形卷积模块的自适应波形模块.传统的卷积神经网络是传统的卷积神经网络.直角时间频率空间.信号检测 信号检测 信号检测

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结论:

  • 在延迟多普勒域中,AWCNN为OTFS信号特征提供了一种更为内在匹配的方法.
  • AWCNN提供了卓越的检测性能和更快的融合,使其成为OTFS系统的一个有前途的技术.
  • 整合自适应波段层和增强的输入功能显著提高了检测准确性和效率.