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arXiv 2104.06309eess.SPcs.AI
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太赫兹传感的信号处理和机器学习技术综述

Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview

Effat University(埃法特大学);King Abdullah University of Science and Technology(阿卜杜拉国王科技大学)

Sara Helal, Hadi Sarieddeen, Hayssam Dahrouj, Tareq Y. Al-Naffouri, Mohamed Slim Alouini

摘要:

随着太赫兹(THz)信号产生和辐射方法的最新进展,太赫兹通信和传感应用正在塑造无线系统的未来。为此,THz光谱预计将在用户设备设备上进行,以识别感兴趣的材料和气体成分。太赫兹特定的信号处理技术应该补充这种重新高涨的兴趣,在太赫兹传感的有效利用太赫兹波段。在本文中,我们提出了这些技术的概述,重点是信号预处理(标准正态变量归一化,最小-最大归一化和Savitzky-Golay滤波),特征提取(主成分分析,偏最小二乘法,t分布随机邻居嵌入,非负矩阵分解),和分类技术(支持向量机,k-最近邻,判别分析和朴素贝叶斯)。我们还通过探索深度学习技术在太赫兹波段的有前途的传感能力来解决深度学习技术的有效性。最后,我们调查的性能和复杂性权衡的研究方法的背景下,联合通信和传感;我们激励相应的用例,我们目前在该领域的一些未来的研究方向。

英文摘要:

Following the recent progress in Terahertz (THz) signal generation and radiation methods, joint THz communications and sensing applications are shaping the future of wireless systems. Towards this end, THz spectroscopy is expected to be carried over user equipment devices to identify material and gaseous components of interest. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. In this paper, we present an overview of these techniques, with an emphasis on signal pre-processing (standard normal variate normalization, min-max normalization, and Savitzky-Golay filtering), feature extraction (principal component analysis, partial least squares, t-distributed stochastic neighbor embedding, and nonnegative matrix factorization), and classification techniques (support vector machines, k-nearest neighbor, discriminant analysis, and naive Bayes). We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band. Lastly, we investigate the performance and complexity trade-offs of the studied methods in the context of joint communications and sensing; we motivate the corresponding use-cases, and we present few future research directions in the field.

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。

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