受生物启发的生物纳米机器集群集体梯度检测方法的性能分析

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2024-09-26 DOI:10.1109/LCOMM.2024.3469130

Mayuka Chiba;Tadashi Nakano

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引用次数: 0

摘要

在分子通信中，信息可以编码在信号分子的浓度梯度上，因此梯度检测是一个重要问题。这封信探讨了一种受生物启发的方法，让生物纳米机器集群进行集体梯度检测。它建立了集体梯度检测方法的数学模型，并利用该模型分析了其性能。信中报告的主要发现之一是存在一个能使梯度检测性能最大化的最佳集群规模。这封信的贡献包括探索了分子通讯的一个新研究方向，即把生物纳米机器集群作为分子通讯系统的一个单元。

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Performance Analysis of a Biologically Inspired Collective Gradient Detection Method for a Bio-Nanomachine Cluster

In molecular communication, information can be encoded on the concentration gradient of signal molecules, making gradient detection an important issue. This letter considers a biologically inspired method for a cluster of bio-nanomachines to perform collective gradient detection. It develops a mathematical model of the collective gradient detection method, and uses the model to analyze its performance. One of the major findings reported in this letter is the existence of an optimal cluster size that can maximize the gradient detection performance. The contributions of this letter include exploring a new research direction in molecular communication, where a cluster of bio-nanomachines is used as a unit of molecular communication systems.

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.