Hybrid Recurrent Neural Network for Signal-Dependent Noise Suppression in Molecular Communication

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2025-02-26 DOI:10.1109/TMBMC.2025.3546208

Cheng Xiang;Yaqing Zhang;Yu Huang;Weiqiang Tan;Xuan Chen;Miaowen Wen

引用次数: 0

Abstract

Molecular communication (MC) employs chemical molecules for information transfer in environments where electromagnetic signals are ineffective. However, the diffusion mechanism introduces signal-dependent noise (SDN), complicating accurate signal recovery. Traditional model-based methods struggle to handle SDN’s complex dynamics and depend heavily on optimal parameter tuning, limiting their adaptability to temporal variations. To tackle these challenges, this paper introduces a hybrid recurrent neural network (RNN) model that effectively captures both short- and long-term dependencies within MC signals, surpassing the performance of single RNN models and traditional approaches. This model offers a promising data-driven solution for noise mitigation in MC, with its effectiveness validated through numerical simulation results.

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基于混合递归神经网络的分子通信信号依赖噪声抑制

分子通信（MC）利用化学分子在电磁信号无效的环境中进行信息传递。然而，扩散机制引入了信号相关噪声（SDN），使精确的信号恢复变得复杂。传统的基于模型的方法难以处理SDN的复杂动态，并且严重依赖于最优参数调整，限制了它们对时间变化的适应性。为了解决这些挑战，本文引入了一种混合循环神经网络（RNN）模型，该模型有效地捕获了MC信号中的短期和长期依赖关系，超越了单一RNN模型和传统方法的性能。该模型为MC噪声抑制提供了一种有前景的数据驱动解决方案，并通过数值模拟结果验证了其有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Mathematics-Modeling and Simulation

CiteScore

3.90

自引率

13.60%

发文量

期刊介绍： As a result of recent advances in MEMS/NEMS and systems biology, as well as the emergence of synthetic bacteria and lab/process-on-a-chip techniques, it is now possible to design chemical “circuits”, custom organisms, micro/nanoscale swarms of devices, and a host of other new systems. This success opens up a new frontier for interdisciplinary communications techniques using chemistry, biology, and other principles that have not been considered in the communications literature. The IEEE Transactions on Molecular, Biological, and Multi-Scale Communications (T-MBMSC) is devoted to the principles, design, and analysis of communication systems that use physics beyond classical electromagnetism. This includes molecular, quantum, and other physical, chemical and biological techniques; as well as new communication techniques at small scales or across multiple scales (e.g., nano to micro to macro; note that strictly nanoscale systems, 1-100 nm, are outside the scope of this journal). Original research articles on one or more of the following topics are within scope: mathematical modeling, information/communication and network theoretic analysis, standardization and industrial applications, and analytical or experimental studies on communication processes or networks in biology. Contributions on related topics may also be considered for publication. Contributions from researchers outside the IEEE’s typical audience are encouraged.