The problem of signal detection and modulation recognition is addressed in both the blind and nonblind contexts. Many relevant modulation recognition algorithms have been created over the past three decades. The essential engineering trade-off that fuels the algorithm-invention process is between generality and optimality. Optimal (for example, maximum-likelihood) algorithms can be devised for narrow subsets of signal types, whereas highly general but suboptimal feature-based algorithms can be devised for wide subsets of types. It has previously been shown that cyclostationary-based classifiers possess highly desirable properties, such as tolerance to noise and cochannel interference, but also that they are computational costly and so are resistant to real-time hardware implementation. In this paper, we present a reduced-complexity method of signal detection and modulation recognition that exploits the cyclostationarity of over-the-air observed communication signals. The key idea is to represent wideband input data as a set of narrowband, contiguous, nonoverlapping subchannels, and then to extract the desired cyclostationary features for the wideband data using only sparse subsets of the subchannels.

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.

The paper presents a novel deep-learning (DL) based classifier for digitally modulated signals that uses a capsule network (CAP) with custom-designed feature extraction layers. The classifier takes the in-phase/quadrature (I/Q) components of the digitally modulated signal as input, and the feature extraction layers are inspired by cyclostationary signal processing (CSP) techniques, which extract the cyclic cumulant (CC) features that are employed by conventional CSP-based approaches to blind modulation classification and signal identification. Specifically, the feature extraction layers implement a proxy of the mathematical functions used in the calculation of the CC features and include a squaring layer, a raise-to-the-power-of-three layer, and a fast-Fourier-transform (FFT) layer, along with additional normalization and warping layers to ensure that the relative signal powers are retained and to prevent the trainable neural network (NN) layers from diverging in the training process. 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 feature extraction layers achieves high classification accuracy while also outperforming alternative DL-based approaches for signal classification in terms of both classification accuracy and generalization abilities.

This paper presents a novel filter for mitigating the interference of pulsed chirp radar signals to orthogonal frequency-division multiplexing (OFDM) signals, reducing the size of exclusion zones or dependence on spectrum access systems. The method exploits the spectral redundancies in OFDM and radar signals using a time-varying frequency shift filter. The simulated results show the performance improvement in both the bit error rate and the signal-to-interference-and-noise ratio.

Cognitive radios must examine the contents of the RF bands potentially available for communication prior to commencing transmission. This process is often referred to as spectrum sensing, and its goal is the declaration of subbands as either occupied (black space) or unoccupied (white space). If the radio is equipped with an OFDM waveform, even a set of irregularly spaced narrow white subbands can be useful by properly selecting the subset of subcarriers on which to transmit. It is important, therefore, to provide the radio with the ability to automatically detect all available white space for a wide variety of RF scenes: densely packed emitters, arbitrarily colored background noise, mixtures of emitter bandwidths, etc. In other contexts, such as with the wideband single-carrier MUOS (Mobile User Objective System) satellite communication system, the black spaces are avoided by an adaptive multi-notch transmit filter whose passbands reflect the detected white spaces. In this paper, a novel automatic multi-resolution general-purpose white-space detector is described. To illustrate the difficulty of the problem and the potential of the algorithmic solution, the method is applied to simulated and collected UHF data sets.

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).