Fourth order cumulants in distinguishing single carrier from OFDM signals

Shi, Miao; Laufer, Amir; Bar-Ness, Y.; Su, Wei

doi:10.1109/milcom.2008.4753541

Cited by 19 publications

(12 citation statements)

References 3 publications

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“…Using these moments in the corrected expression in (24) gives that J var[ Ĉ42 ] ≈ 4 which coincidentally also matches the result derived in [19] from the incorrect expression of [15]. Note however that this result only applies for OFDM with subcarrier modulations that satisfy the fourfold symmetry such as QPSK.…”

Section: B Statistics For Ofdma Signalssupporting

confidence: 72%

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Channel Access Method Classification for Cognitive Radio Applications

Laghate

Urriza

Čabrić

2018

IEEE Wireless Commun. Lett.

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Abstract-Motivated by improved detection and prediction of temporal holes, we propose a two stage algorithm to classify the channel access method used by a primary network. The first stage extends an existing fourth-order cumulant-based modulation classifier to distinguish between TDMA, OFDMA, and CDMA. The second stage proposes a novel collision detector using the sample variance of the same cumulant to detect contentionbased channel access methods. Our proposed method is blind and independent of the received SNR. Simulations show that our classification of TDMA, OFDMA, and CDMA is robust to network load while detection of contention outperforms existing methods.

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Section: B Statistics For Ofdma Signalssupporting

confidence: 72%

“…The use of fourth order cumulants for distinguishing OFDM signals from single carrier signals is proposed and analyzed in [19]. The moments for OFDM are found to be…”

Section: B Statistics For Ofdma Signalsmentioning

confidence: 99%

Channel Access Method Classification for Cognitive Radio Applications

Laghate

Urriza

Čabrić

2018

IEEE Wireless Commun. Lett.

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“…TR has been mainly applied to the identification of communication systems based on the differentiation of their channel method access, e.g., Single Carrier (SC) vs. Multiple Carrier (MC) [11], and it has been extended to classify various wireless communication technology standards, e.g., WiMAX vs. LTE [12]. Similar to other related tasks in radio signal identification, traditional approaches for TR are based on Likelihood-Based (LB), and Feature-Based (FB) using highorder statistics features such as moments, cumulants and cyclic cumulants.…”

Section: Related Workmentioning

confidence: 99%

A semi-supervised learning approach towards automatic wireless technology recognition

Camelo

Shahid

Fontaine

et al. 2019

2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

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Radio spectrum has become a scarce commodity due to the advent of several non-collaborative radio technologies that share the same spectrum. Recognizing a radio technology that accesses the spectrum is fundamental to define spectrum management policies to mitigate interference. State-of-the-art approaches for technology recognition using machine learning are based on supervised learning, which requires an extensive labeled data set to perform well. However, if the technologies and their environment are entirely unknown, the labeling task becomes time-consuming and challenging. In this work, we present a Semisupervised Learning (SSL) approach for technology recognition that exploits the capabilities of modern Software Defined Radios (SDRs) to build large unlabeled data sets of IQ samples but requires only a few of them to be labeled to start the learning process. The proposed approach is implemented using a Deep Autoencoder, and the comparison is carried out against a Supervised Learning (SL) approach using Deep Neural Network (DNN). Using the DARPA Colosseum test bed, we created an IQ sample data set of 16 unknown radio technologies and obtain a classification accuracy of > 97% using the entire labeled data set using both approaches. However, the proposed SSL approach achieves a classification accuracy of ≥ 70% while using only 10% of the labeled data. This performance is equivalent to 4.6x times better classification accuracy than the DNN using the same reduced labeled data set. More importantly, the proposed approach is more robust than the DNN under corrupted input, e.g., noisy signals, which gives us to 2x and 3x better accuracy at Signal-to-Noise Ratio (SNR) of -5 dB and 0 dB, respectively.

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“…Secondly in [6], a method based on the combination of fourth order moments and the amplitude estimation is introduced (AE-M) and third, sums of fourth order cumulants are used for OFDM v.s. SC classification in [4] (SUM-C). The coherent method employs a two stage identification procedure to distinguish SLCD, BT-SLCD, and OFDM signals from each other based on the unique second order cyclostationary features of these signals.…”

Section: Theoremmentioning

confidence: 99%

An OFDM Signal Identification Method for Wireless Communications Systems

Görçin

Arslan

2015

IEEE Trans. Veh. Technol.

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1 Abstract-Distinction of OFDM signals from single carrier signals is highly important for adaptive receiver algorithms and signal identification applications. OFDM signals exhibit Gaussian characteristics in time domain and fourth order cumulants of Gaussian distributed signals vanish in contrary to the cumulants of other signals. Thus fourth order cumulants can be utilized for OFDM signal identification. In this paper, first, formulations of the estimates of the fourth order cumulants for OFDM signals are provided. Then it is shown these estimates are affected significantly from the wireless channel impairments, frequency offset, phase offset and sampling mismatch. To overcome these problems, a general chi-square constant false alarm rate Gaussianity test which employs estimates of cumulants and their covariances is adapted to the specific case of wireless OFDM signals. Estimation of the covariance matrix of the fourth order cumulants are greatly simplified peculiar to the OFDM signals. A measurement setup is developed to analyze the performance of the identification method and for comparison purposes. A parametric measurement analysis is provided depending on modulation order, signal to noise ratio, number of symbols, and degree of freedom of the underlying test. The proposed method outperforms statistical tests which are based on fixed thresholds or empirical values, while a priori information requirement and complexity of the proposed method are lower than the coherent identification techniques.

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Fourth order cumulants in distinguishing single carrier from OFDM signals

Cited by 19 publications

References 3 publications

Channel Access Method Classification for Cognitive Radio Applications

Channel Access Method Classification for Cognitive Radio Applications

A semi-supervised learning approach towards automatic wireless technology recognition

An OFDM Signal Identification Method for Wireless Communications Systems

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