通过扩散实现多跳移动分子协同通信的信号检测

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2024-01-30 DOI:10.1109/TMBMC.2024.3360341

Zhen Cheng;Zhichao Zhang;Jie Sun

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引用次数: 0

摘要

基于深度学习的数据驱动检测器在具有未知信道参数的分子扩散通信（MCvD）系统信号检测中有着广阔的应用前景。本文提出了一种基于变压器模型的信号检测器，用于采用放大-前向中继策略的合作多跳移动 MCvD 系统。在考虑多分子型（MMT）和单分子型（SMT）两种传输方案时，得出接收分子数的数学表达式，从而生成训练数据集。在此基础上，利用训练数据集离线训练基于 Transformer 的模型。然后，在 MMT 和 SMT 条件下，采用训练好的基于变换器的模型来检测未知信道参数下的接收信号。数值结果表明，与深度神经网络（DNN）检测器和卷积神经网络（CNN）检测器相比，基于变换器的模型在合作多跳移动 MCvD 系统中的检测能力最强，信号检测误码率最低。

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Signal Detection of Cooperative Multi-Hop Mobile Molecular Communication via Diffusion

The data-driven detectors based on deep learning have promising applications in signal detection with unknown channel parameters of molecular communication via diffusion (MCvD) system. In this paper, a signal detector for cooperative multi-hop mobile MCvD system with amplify-forward relaying strategy by using Transformer-based model is proposed. The mathematical expressions of the numbers of received molecules when considering two transmission schemes including multi-molecule-type (MMT) and single-molecule-type (SMT) are derived in order to generate the training dataset. On this basis, the training dataset is used to train the Transformer-based model offline. Then the trained Transformer-based model is adopted to detect the received signal under unknown channel parameters under MMT and SMT. Numerical results show that the Transformer-based model performs the best detection ability in cooperative multi-hop mobile MCvD system with lowest bit error rate of signal detection compared with deep neural networks (DNN) detector and convolutional neural networks (CNN) detector.

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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.