Artificial intelligence for molecular communication

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Max Bartunik, J. Kirchner, Oliver Keszöcze
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引用次数: 1

Abstract

Abstract Molecular communication is a novel approach for data transmission between miniaturised devices, especially in contexts where electrical signals are to be avoided. The communication is based on sending molecules (or other particles) at nanoscale through a typically fluid channel instead of the “classical” approach of sending electrons over a wire. Molecular communication devices have a large potential in future medical applications as they offer an alternative to antenna-based transmission systems that may not be applicable due to size, temperature, or radiation constraints. The communication is achieved by transforming a digital signal into concentrations of molecules that represent the signal. These molecules are then detected at the other end of the communication channel and transformed back into a digital signal. Accurately modeling the transmission channel is often not possible which may be due to a lack of data or time-varying parameters of the channel (e.g., the movements of a person wearing a medical device). This makes the process of demodulating the signal (i.e., signal classification) very difficult. Many approaches for demodulation have been discussed in the literature with one particular approach having tremendous success – artificial neural networks. These artificial networks imitate the decision process in the human brain and are capable of reliably classifying even rather noisy input data. Training such a network relies on a large set of training data. As molecular communication as a technology is still in its early development phase, this data is not always readily available. In this paper, we discuss neural network-based demodulation approaches relying on synthetic simulation data based on theoretical channel models as well as works that base their network on actual measurements produced by a prototype test bed. In this work, we give a general overview over the field molecular communication, discuss the challenges in the demodulations process of transmitted signals, and present approaches to these challenges that are based on artificial neural networks.
用于分子通信的人工智能
摘要分子通信是一种在微型设备之间进行数据传输的新方法,尤其是在需要避免电信号的情况下。这种通信是基于通过典型的流体通道在纳米级发送分子(或其他粒子),而不是通过导线发送电子的“经典”方法。分子通信设备在未来的医疗应用中具有巨大的潜力,因为它们提供了一种基于天线的传输系统的替代方案,由于尺寸、温度或辐射限制,这些系统可能不适用。通信是通过将数字信号转换为代表信号的分子浓度来实现的。然后在通信通道的另一端检测到这些分子,并将其转换回数字信号。准确地建模传输信道通常是不可能的,这可能是由于缺乏数据或信道的时变参数(例如,佩戴医疗设备的人的运动)。这使得解调信号(即信号分类)的过程非常困难。文献中讨论了许多解调方法,其中一种方法取得了巨大成功——人工神经网络。这些人工网络模仿了人类大脑中的决策过程,即使是相当嘈杂的输入数据也能够可靠地分类。训练这样的网络依赖于大量的训练数据。由于分子通信技术仍处于早期发展阶段,因此这些数据并不总是现成的。在本文中,我们讨论了基于理论信道模型的合成模拟数据的神经网络解调方法,以及基于原型测试台产生的实际测量值的网络解调工作。在这项工作中,我们对分子通信领域进行了概述,讨论了传输信号解调过程中的挑战,并提出了基于人工神经网络的解决这些挑战的方法。
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
IT-Information Technology
IT-Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.80
自引率
0.00%
发文量
29
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