USRP N210 demonstration of wideband sensing and blind hierarchical modulation classification

Laghate, Mihir; Chaudhari, Shailesh; Čabrić, Danijela

doi:10.1109/dyspan.2017.7920748

Cited by 14 publications

(5 citation statements)

References 7 publications

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“…In [21][22][23] several spectrum sensing algorithms, including those based on cyclostationary features, have been implemented and tested using GNU Radio/USRP. In [24] cyclostationary features are used to perform hierarchical modulation clas-sification, and the resulting algorithms are implemented in C++ and tested using GNU Radio/USRP. Recently, in [25] cyclostationary feature detectors for spectrum sensing are implemented in the GNU Radio/USRP environment, which are characterized by a reduced design complexity in comparison to other solutions in the literature.…”

Section: Introductionmentioning

confidence: 99%

CycloDSP: A cyclostationary signal analysis tool for GNU Radio

Darsena,

Gelli,

Iudice

2024

2024 IEEE Aerospace Conference

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In this paper, we present a first attempt to incorporate in the GNU Radio ecosystem a tool called CycloDSP, devoted to the analysis of complex-valued cyclostationary signals. Such signals are ubiquitous in communication and signal processing, exhibiting periodic or almost periodic statistics that are characterized by a countable set of cycle frequencies, which are related to the main signal periodicities. Common cycle frequencies for modulated signals are multiple of the baudrate and/or combination of the carrier frequency and baudrate. Conventional estimation strategies for cyclostationary signal analysis typically exhibit a high computational burden. Many approaches aimed at reducing complexity exploit fast Fourier transform (FFT) algorithms, which are very efficient for batch data but are not suited for continuously streaming data, typically encountered in software-defined radio (SDR) applications. The aim of this paper is to develop an out-of-tree (OOT) GNU Radio module containing a set of building block functions, aimed at estimating functions, such as the cyclic correlation functions, typically employed for cyclostationary signal analysis. The proposed implementation must be designed so as to ensure high efficiency in processing continuous data streams of complex samples, both in terms of computational load and data storage, in order to be executed on general purpose computers. We tested our implementation by estimating in real-time the second-order cyclic statistics of a Gaussian minimum-shift keying (GMSK) modulated signal, which belongs to a class of signals commonly employed in aeronautical telemetry datalinks.

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Section: Introductionmentioning

confidence: 99%

CycloDSP: A cyclostationary signal analysis tool for GNU Radio

Darsena,

Gelli,

Iudice

2024

2024 IEEE Aerospace Conference

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“…Besides, the performances of classification of higher-order modulation schemes are mostly unsatisfactory at low signal-to-noise ratio (SNR) values [46,47]. A few hierarchical classifiers have been reported [48,49] however they seem to be impractical as they utilize many stages and classifiers which makes them highly complex. Considering the trade-off of the low complexity and high success, these types of structures generally remain insufficient despite their high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Classification of Analog and Digital Modulation Schemes using Higher-Order Statistics and Support Vector Machines

Gokdogan

Coruk

Kara

et al. 2022

Preprint

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Automatic Modulation Classification (AMC) algorithms play an important role in various military and civilian applications. There have been numerous AMC algorithms reported in the literature, most of which focus on synthetic signals with a limited number of modulation types having distinctive constellations. The efficient classification of high-order modulation schemes under real propagation effects using models with low complexity is still a challenge. In this paper, employing quadratic SVM, a feature-based (FB) hierarchical classification method is proposed to accurately classify especially higher-order modulation schemes and its performance is investigated using over the air (OTA) collected data. Statistical features, higher-order moments, and higher-order cumulants are employed as features. Then, the performances of some well-known classifiers are evaluated, and the classifier presenting the best performance is employed in the proposed hierarchical classification model. An OTA dataset containing 17 analog and digital modulation schemes is used to evaluate the performance of the proposed classification model. With the proposed hierarchical classification algorithm, a significant improvement has been achieved, especially in higher-order modulation schemes. The overall accuracy with the proposed hierarchical structure is 96% after 5 dB signal-to-noise ratio (SNR) value, approximately a 10% increase is achieved compared to the traditional classification algorithm.

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“…The constellation identification of QAM over frequencyselective fading channels is essential in signal processing [6]. For the QAM constellation identification, [7] employed high-order cumulants and signal cyclostationarity to identify 4/16/64QAM. Reference [8] adopted the Wigner-Ville distribution (WVD) time-frequency to transform the incoming signal, calculate the signal's Renyi entropy, and then leverage the Dempster-Shafer theory to identify the QAM.…”

Section: Introductionmentioning

confidence: 99%