Novel Nonlinear Neural-Network Layers for High Performance and Generalization in Modulation-Recognition Applications

The paper presents a novel type of capsule network (CAP) that uses custom-defined neural network (NN) layers for blind classification of digitally modulated signals using their in-phase/quadrature (I/Q) components. The custom NN layers of the CAP are inspired by cyclostationary signal processing (CSP) techniques and implement feature extraction capabilities that are akin to the calculation of the cyclic cumulant (CC) features employed in conventional CSP-based approaches to blind modulation classification and signal identification. The classification performance and the generalization abilities of the proposed CAP are tested using two distinct datasets that contain similar classes of digitally modulated signals but that have been generated independently, and numerical results obtained reveal that the proposed CAP with novel NN feature extraction layers achieves high classification accuracy while also outperforming alternative deep learning (DL)-based approaches for signal classification in terms of both classification accuracy and generalization abilities.Comment: 6 pages, 7 figures, to be published in IEEE MILCOM 2023: IEEE Military Communications Conference 2023. arXiv admin note: text overlap with arXiv:2211.0023

Spectral Correlation for Signal Presence Detection and Frequency Acquisition of Small Satellites

17 USC 105 interim-entered record; under temporary embargo.Challenges in interference-limited satellite detection arising from the low-earth orbit (LEO) and the Industrial, Scientific and Medical (ISM) frequency bands are addressed. In particular, a novel signal presence detector based on cyclostationary signal properties is proposed and analyzed for a low signal-to-noise-plus-interference ratio (SINR) regime. The performance of the proposed detector, which is applicable to various small-satellite scenarios, is evaluated on both simulated and real-world measurement data. This measurement data has been collected from the scientific satellite mission “Picosats Realizing Orbital Propagation Calibrations using Beacon Emitters” (PROPCUBE).U.S. Government affiliation is unstated in article text

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

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

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