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通过深度神经网络进行异常扩散的移动分子通信的通道建模
IEEE transactions on nanobioscience
|January 19, 2026
概括
本研究引入了深度神经网络 (DNN) 模型,用于预测异常扩散的移动分子通信 (MMC) 系统中的通道冲动响应 (CIR). 与RNN和LSTM模型相比,DNN模型显示出更高的预测准确性,增强了MMC通道建模.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 移动分子通信 (MMC) 系统对纳米级应用具有前景,特别是异常扩散.
- 在MMC中具有多个接收器的异常扩散通道的准确建模具有挑战性.
- 目前用于道脉冲响应 (CIR) 的分析方法仅限于正常的扩散和静态条件,无法适应复杂的,时间变化的环境.
研究的目的:
- 开发和评估一种新的基于深度神经网络 (DNN) 的方法,用于预测在异常扩散下具有多个接收器的3D MMC系统中的CIR参数.
- 将拟议的DNN模型的性能与反复神经网络 (RNN) 和长短期记忆 (LSTM) 模型进行比较,以在异常扩散场景中进行CIR预测.
主要方法:
- 建立了一个具有一个发射器和多个接收器的三维 (3D) 移动分子通信 (MMC) 系统模型.
- 一个深度神经网络 (DNN) 被设计和训练,以在异常扩散条件下预测通道脉冲响应 (CIR) 参数.
- 通过模拟,DNN模型的预测性能与RNN和LSTM模型进行了比较.
主要成果:
- 基于DNN的模型在预测各种异常扩散条件中的CIR参数方面明显优于RNN和LSTM模型.
- 拟议的DNN方法证明了对MMC系统复杂道特征的强大和准确的预测能力.
- 模拟结果验证了DNN模型在提高CIR预测准确性的有效性.
结论:
- 深度神经网络为在移动分子通信 (MMC) 系统中建模异常扩散通道提供了强大而有效的方法.
- 开发的DNN模型在准确预测CIR方面取得了重大进展,这对于在复杂的纳米环境中可靠的通信至关重要.
- 这项工作提出了一种新的方法来增强MMC系统中的通道建模,特别是那些在异常扩散条件下运行的系统.
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