卷积神经网络用于分子指数调制
Molecular Index Modulation using Convolutional Neural Networks
摘要：
随着扩散分子通信（MCvD）系统在纳米级通信中的潜力的增加，设计对分子干扰的不可避免的影响鲁棒的分子方案已变得至关重要。文献中有许多分子方法旨在减轻干扰的影响，即符号间干扰。此外，对于分子多输入多输出系统，天线之间的干扰，即链路间干扰，变得非常重要。受机器学习算法在决策方面的最先进性能的启发，我们提出了一种基于卷积神经网络（CNN）架构的新方法。所提出的方法是一个巧妙设计的分子多输入单输出拓扑结构，以减轻分子干扰的破坏性影响。在这项研究中，我们比较了所提出的网络的索引调制方法和符号的最大似然估计的性能，并表明所提出的方法产生更好的性能。
英文摘要：
As the potential of molecular communication via diffusion (MCvD) systems at nano-scale communication increases, designing molecular schemes robust to the inevitable effects of molecular interference has become of vital importance. There are numerous molecular approaches in literature aiming to mitigate the effects of interference, namely inter-symbol interference. Moreover, for molecular multiple-input-multiple-output systems, interference among antennas, namely inter-link interference, becomes of significance. Inspired by the state-of-the-art performances of machine learning algorithms on making decisions, we propose a novel approach of a convolutional neural network (CNN)-based architecture. The proposed approach is for a uniquely-designed molecular multiple-input-single-output topology in order to alleviate the damaging effects of molecular interference. In this study, we compare the performance of the proposed network with that of an index modulation approach and a symbol-by-symbol maximum likelihood estimation and show that the proposed method yields better performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
