Robust Signal Classification Using Unsupervised Learning

Clancy, T. Charles; Khawar, Awais; Newman, T. J.

doi:10.1109/twc.2011.030311.101137

Cited by 42 publications

(30 citation statements)

References 11 publications

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“…Since [70], more recent works on the use of machine learning for spectrum sensing include [71] that used artificial neural network technique to detect primary user in low signal-to-noise ratio scenarios, [72] that used unsupervised learning to evolve the classifier in sensing with security countermeasures, [73] that used support vector machine to outperform energy detection, [74] that used unsupervised K-means clustering and Gaussian mixture model as well as supervised support vector machine and K-nearest neighbour for cooperative spectrum sensing, and [75] that also used support vector machine to detect weak primary user signals, to name a few. These works do not use measurement data for verification.…”

Section: Spectrum Occupancy Predictionmentioning

confidence: 99%

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Chen

2016

IEEE Commun. Surv. Tutorials

205

105

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Section: Spectrum Occupancy Predictionmentioning

confidence: 99%

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Chen

2016

IEEE Commun. Surv. Tutorials

205

105

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“…To combat this problem, we propose that k-means employs randomly three features of the received signal and find its distance from the three corresponding features of the cluster centers, then decides to which cluster the signal should be assigned. Figure 2 presents clustering of I = 30 received signals into N = 3 clusters, using three features: kurtosis of square of signal, kurtosis of d/dt of the signal, and autocorrelation of the signal [7]. In this example, PUn signal is BPSK, 16QAM and GMSK for n = 1, 2, 3, respectively.…”

Section: Enhanced K-means Algorithmmentioning

confidence: 99%

Secure Cognitive Radio Communication for Internet-of-Things: Anti-PUE Attack Based on Graph Theory

Hosseini¹,

Abolhassani²,

Hosseini³

2017

JCC

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Internet of Things (IoT) paradigm with strong impact on future life will be interconnected through Cognitive Radio Networks (CRNs). CRNs with Ubiquitous trait are highly promising to achieve interference-free and on-demand services. CRs are able to sense the spectral environment, to detect unoccupied bands, and to use them for signal transmissions. This opportunity encourages malicious Users to surpass CRs by Primary User Emulation (PUE) attack and use vacant spectrums. This paper proposes an unsupervised algorithm to distinguish CRs from PUs regardless of static and mobile user. Employing K-means and graph theory are coincident in our algorithm to improve detection outcomes. The edge of graph corresponding to the relation between signals is used and the result of comparison the signal properties is exposed to different clusters. The Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) of our proposed algorithm prove our claim.

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“…This makes unsupervised learning algorithms more appealing for CR applications, compared to supervised learning, since they lead to autonomous cognitive behavior in the absence of instructors [9]. Hence, unsupervised learning has been the focus of recent autonomous CRs formulations [7,9,[119][120][121][122][123]. Several unsupervised learning algorithms have been proposed for CRs to perform either feature classification or decision-making [29].…”

Section: The Cognitive Enginementioning

confidence: 99%

Cognitive Radio Transceivers: RF, Spectrum Sensing, and Learning Algorithms Review

Safatly

Bkassiny²,

Al‐Husseini³

et al. 2014

International Journal of Antennas and Propagation

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A cognitive transceiver is required to opportunistically use vacant spectrum resources licensed to primary users. Thus, it relies on a complete adaptive behavior composed of: reconfigurable radio frequency (RF) parts, enhanced spectrum sensing algorithms, and sophisticated machine learning techniques. In this paper, we present a review of the recent advances in CR transceivers hardware design and algorithms. For the RF part, three types of antennas are presented: UWB antennas, frequency-reconfigurable/tunable antennas, and UWB antennas with reconfigurable band notches. The main challenges faced by the design of the other RF blocks are also discussed. Sophisticated spectrum sensing algorithms that overcome main sensing challenges such as model uncertainty, hardware impairments, and wideband sensing are highlighted. The cognitive engine features are discussed. Moreover, we study unsupervised classification algorithms and a reinforcement learning (RL) algorithm that has been proposed to perform decision-making in CR networks.

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Robust Signal Classification Using Unsupervised Learning

Cited by 42 publications

References 11 publications

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Secure Cognitive Radio Communication for Internet-of-Things: Anti-PUE Attack Based on Graph Theory

Cognitive Radio Transceivers: RF, Spectrum Sensing, and Learning Algorithms Review

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