Using Capsule Networks to Classify Digitally Modulated Signals with Raw I/Q Data

Machine learning has become a powerful tool for solving problems in various engineering and science areas, including the area of communication systems. This paper presents the use of capsule networks for classification of digitally modulated signals using the I/Q signal components. The generalization ability of a trained capsule network to correctly classify the classes of digitally modulated signals that it has been trained to recognize is also studied by using two different datasets that contain similar classes of digitally modulated signals but that have been generated independently. Results indicate that the capsule networks are able to achieve high classification accuracy. However, these networks are susceptible to the datashift problem which will be discussed in this paper.Comment: 6 pages, 9 figures, to be published in IEEE ICC 2022: IEEE International Conference on Communications 202

Deep-Learning-Based Classification of Digitally Modulated Signals Using Capsule Networks and Cyclic Cumulants

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper outperformed alternative approaches for classifying digitally modulated signals that included conventional classifiers that employed CSP-based techniques, as well as alternative DL-based classifiers that used convolutional neural networks (CNNs) or residual networks (RESNETs) with the in-phase/quadrature (I/Q) data used for training and classification

A subpopulation of spinach chloroplast tRNA genes does not require upstream promoter elements for transcription.

We have identified a class of spinach plastid tRNA genes which do not require 5' upstream promoter elements for their expression in a chloroplast transcription system. The 5' DNA sequences flanking the trnR1 and trnS1 coding regions have little or no homology to previously characterized chloroplast promoter sequences. The deletion of the 5' DNA sequences from these genes to positions close to the start of the coding regions has little effect on their transcription in vitro. In addition, a synthetic DNA fragment homologous to the 5' region of trnS1 does not support the transcription of the promoter (-) trnM2 mutant 51 in a promoter/trnM2-51 fusion assay. In a dicistronic construct the wild type trnS1 gene does not support transcription of trnS1 transcription occurs immediately following the 3' end of the coding region. Both trnS1 and trnR1 compete with trnM2 for the same chloroplast RNA polymerase and/or common transcription factors