A new approach to signal classification using spectral correlation and neural networks

Fehske, Albrecht; Gaeddert, Joseph; Reed, Jeffrey H.

doi:10.1109/dyspan.2005.1542629

Cited by 339 publications

(211 citation statements)

References 13 publications

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“…The cyclostationary feature detector relies on the fact that most signals exhibit periodic features, present in pilots, cyclic prefixes, modulations, carriers and other repetitive characteristics [25,26,27,28,29,30]. Since the noise is not periodic, the signal can be successfully detected.…”

Section: Cyclostationary Feature Detectionmentioning

confidence: 99%

“…Some works in the literature [6,7,8] consider spectrum sensing as a method for distinguishing between two or more different types of signals or technologies in operation. Since this is not a question of detection (determining whether a given frequency band is being used), these types of signal identification issues such as [9] are not addressed in this chapter.…”

Section: Characteristics Of Spectrum Sensingmentioning

confidence: 99%

“…There are several classical techniques for this purpose, including the energy detector (ED) [16,21,22], the matched filter (MF) [23,34] and the cyclostationary feature detection (CFD) [25,26,27,28,29].…”

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confidence: 99%

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Spectrum Sensing for Cognitive Radio

2009

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Section: Cyclostationary Feature Detectionmentioning

confidence: 99%

Section: Characteristics Of Spectrum Sensingmentioning

confidence: 99%

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Spectrum Sensing for Cognitive Radio

2009

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“…Known as cyclostationary based detection, this form of spectrum sensing usually only requires knowledge of one or two periodic features in the PU signal to achieve good detection results [56]. It also has the added advantage of being able to distinguish PUs from each other, the background noise and other transmissions [57].…”

Section: ) Feature Based Detectionmentioning

confidence: 99%

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Chiwewe

Mbuya

Hancke

2015

IEEE Trans. Ind. Inf.

133

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Abstract-Industrial wireless sensor networks have to contend with environments that are usually harsh and time varying. Industrial wireless technology, such as WirelessHART and ISA 100.11a, also operates in a frequency spectrum utilised by many other wireless technologies, and with wireless applications rapidly growing it is possible that multiple heterogeneous wireless systems would need to operate in overlapping spatiotemporal regions. Interference such as noise or other wireless devices affects connectivity and reduces communication link quality. This negatively affects reliability and latency, which are core requirements of industrial communication. Building wireless networks that are resistant to noise in industrial environments and can coexist with competing wireless devices in an increasingly crowded frequency spectrum is challenging. To meet these challenges, we need to consider the benefits that approaches finding success in other application areas can offer industrial communication. Cognitive radio methods offer a potential solution to improve resistance of industrial wireless sensor networks to interference. Integrating cognitive radio principles into the lower layers of industrial wireless sensor networks can enable devices to detect and avoid interference and potentially opens the possibility of utilising free radio spectrum for additional communication channels. This improves resistance to noise and increases redundancy in terms of channels per network node or adding additional nodes. In this paper, we summarise cognitive radio methods relevant to industrial applications, covering cognitive radio architecture, spectrum access and interference management, spectrum sensing, dynamic spectrum access, game theory and cognitive radio network security.Index Terms-Cognitive radio, industrial wireless sensor networks, Internet of Things, spectrum management, spectrum sensing.

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“…On the other hand, Tsagkaris et al [11] have introduced and evaluated the learning schemes those are based on artificial neural networks and their study could be used to predict the capabilities (e.g., data rate) of a specific radio configuration. Finally, Fehske et al [12] have used Spectral Correlation Density (SCD) and Spectral Coherence Function (SCF) to perform Multi-layer Perceptron (MLP) to sense the spectrum. As opposed to these methods, the proposed system develops its own modulation technique by using Amplitude Frequency Phase Shift Keying (AFPSK) and the data communication is achieved when the signal waveforms constructed through this method are recognized by MLP.…”

Section: Pbcs Modelmentioning

confidence: 99%

A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

Ustundag

Orcay

2011

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Inefficient utilization of the frequency spectrum due to conventional regulatory limitations and physical performance limiting factors, mainly the Signal to Noise Ratio (SNR), are prominent restrictions in digital wireless communication. Pattern Based Communication System (PBCS) is an adaptive and perceptual communication method based on a Cognitive Radio (CR) approach. It intends an SNR oriented cognition mechanism in the physical layer for improvement of Link Spectral Efficiency (LSE). The key to this system is construction of optimal communication signals, which consist of encoded data in different pattern forms (waveforms) depending on spectral availabilities. The signals distorted in the communication medium are recovered according to the pre-trained pattern glossary by the perceptual receiver. In this study, we have shown that it is possible to improve the bandwidth efficiency when largely uncorrelated signal patterns are chosen in order to form a glossary that represents symbols for different length data groups and the information can be recovered by the Artificial Neural Network (ANN) in the receiver site.

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A new approach to signal classification using spectral correlation and neural networks

Cited by 339 publications

References 13 publications

Spectrum Sensing for Cognitive Radio

Spectrum Sensing for Cognitive Radio

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

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