A frequency domain multiplexing scheme based on kernel density estimation for neural communication systems

IF 2.9 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Nano Communication Networks Pub Date : 2023-10-10 DOI:10.1016/j.nancom.2023.100479
Zhuoqun Jin , Yu Li , Yao Chen , Hao Yan , Lin Lin
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引用次数: 0

Abstract

Transmitting information in engineered neural communication systems is a promising solution to delay-sensitive applications for the Internet of Bio-Nanothings (IoBNTs). As widely used in wired and wireless communication systems, introducing multiplexing into neural communication system could improve channel transmission efficiency. In this paper, we model a neural communication system for IoBNTs and propose a neural signal multiplexing scheme for this system, based on frequency-division multiplexing (FDM) principles. The whole system including channel modeling, neural encoding, demultiplexing scheme, and decoding method using kernel density estimation (KDE) are presented. The optimal parameters for KDE and bit error probability are analyzed, and the performance of the proposed strategy is evaluated in terms of error rate and mutual information rate. The work can help researchers better understanding the underlying mechanism of neural multiplexing and pave the way for the implementation of IoBNT applications.

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一种基于核密度估计的神经通信系统频域复用方案
在工程神经通信系统中传输信息是生物纳米技术互联网(IoBNTs)延迟敏感应用的一种很有前途的解决方案。由于在有线和无线通信系统中广泛使用,在神经通信系统中引入多路复用可以提高信道传输效率。在本文中,我们对IoBNT的神经通信系统进行了建模,并基于频分复用(FDM)原理为该系统提出了一种神经信号复用方案。介绍了整个系统,包括信道建模、神经编码、解复用方案和使用核密度估计(KDE)的解码方法。分析了KDE的最优参数和误码率,并从误码率和互信息率两个方面评估了该策略的性能。这项工作可以帮助研究人员更好地理解神经复用的潜在机制,并为IoBNT应用的实现铺平道路。
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来源期刊
Nano Communication Networks
Nano Communication Networks Mathematics-Applied Mathematics
CiteScore
6.00
自引率
6.90%
发文量
14
期刊介绍: The Nano Communication Networks Journal is an international, archival and multi-disciplinary journal providing a publication vehicle for complete coverage of all topics of interest to those involved in all aspects of nanoscale communication and networking. Theoretical research contributions presenting new techniques, concepts or analyses; applied contributions reporting on experiences and experiments; and tutorial and survey manuscripts are published. Nano Communication Networks is a part of the COMNET (Computer Networks) family of journals within Elsevier. The family of journals covers all aspects of networking except nanonetworking, which is the scope of this journal.
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