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

Following the recent progress in terahertz (THz) signal generation and radiation methods, joint THz communications and sensing (CAS) applications are being proposed for future wireless systems. Toward 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 resurgent interest in THz sensing for efficient utilization of the THz band. In this article, we present an overview of these techniques, with an emphasis on signal preprocessing [standard normal variate (SNV) normalization, minimum-maximum normalization, and Savitzky-Golay (SG) filtering], feature extraction [principal component analysis (PCA), partial least squares (PLS), t-distributed stochastic neighbor embedding (t-SNE), and nonnegative matrix factorization (NMF)], and classification techniques [support vector machines (SVMs), the k-nearest neighbor (kNN), discriminant analysis (DA), and naive Bayes (NB)]. We also address the effectiveness of deep learning techniques by exploring their promising sensing and localization capabilities at the THz band. Finally, we investigate the performance and complexity tradeoffs of the studied methods in the context of joint CAS (JCAS). We thereby motivate corresponding use cases and present a handful of contextual future research directions.