Cyclostationarity based air interface recognition for software radio systems

Oner, Menguc; Jondral, Friedrich K.

doi:10.1109/rawcon.2004.1389125

Cited by 86 publications

(46 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore it is reasonable to assume that cognitive radios must be able to detect OFDM signals. The structure of OFDM signals with a cyclic prefix (CP) gives a well known and useful cyclostationarity property [9]. Detectors that utilize this property have been derived, for example in [10], [11], [12] using the autocorrelation property, and in [13] using multiple cyclic frequencies.…”

Section: Introductionmentioning

confidence: 99%

Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance

Axell

Larsson

2011

IEEE J. Select. Areas Commun.

136

123

View full text Add to dashboard Cite

Abstract-We consider spectrum sensing of OFDM signals in an AWGN channel. For the case of completely known noise and signal powers, we set up a vector-matrix model for an OFDM signal with a cyclic prefix and derive the optimal NeymanPearson detector from first principles. The optimal detector exploits the inherent correlation of the OFDM signal incurred by the repetition of data in the cyclic prefix, using knowledge of the length of the cyclic prefix and the length of the OFDM symbol. We compare the optimal detector to the energy detector numerically. We show that the energy detector is near-optimal (within 1 dB SNR) when the noise variance is known. Thus, when the noise power is known, no substantial gain can be achieved by using any other detector than the energy detector.For the case of completely unknown noise and signal powers, we derive a generalized likelihood ratio test (GLRT) based on empirical second-order statistics of the received data. The proposed GLRT detector exploits the non-stationary correlation structure of the OFDM signal and does not require any knowledge of the noise power or the signal power. The GLRT detector is compared to state-of-the-art OFDM signal detectors, and shown to improve the detection performance with 5 dB SNR in relevant cases.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance

Axell

Larsson

2011

IEEE J. Select. Areas Commun.

136

123

View full text Add to dashboard Cite

show abstract

“…From the initial work by Gardner (1991) which highlighted that most of the communication signals can be modeled as cyclostationary that exhibits underlying periodicities in their signal structures. Oner and Jondral (2004), proposed Cyclostationarity based air interface recognition for software radio systems. In their work they showed that wireless transmissions in general show very strong cyclostationarity features depending on their modulation type, data rate and carrier frequency etc., especially when excess bandwidth is utilized.…”

Section: Review Of Work On Cyclostationary Feature Based Detectionmentioning

confidence: 99%

A review of cyclostationary feature detection based spectrum sensing technique in cognitive radio networks

Adigwe¹,

Okonkwo²

2016

EJSR

View full text Add to dashboard Cite

In wireless communications, the transmitted signals show very strong cyclostationary features based on the modulation type, carrier frequency, and data rate, especially when excess bandwidth is utilized. Therefore, identifying the unique set of features of a particular radio signal for a given wireless access system can be used to detect the system based on the cyclostationary analysis at the cognitive radio node. Cyclostationary feature detection based technique has been proposed by many researchers as a key solution for detecting these signals. This paper therefore, took a detail review of the proposals from different authors and pointed out the challenges faced by this method and concluded that it cannot be used to achieve optimality when detecting the presence and absence of primary user. The paper also went further to analyze the principle and model for cyclostationary feature based detection method.

show abstract

“…However, we have to take note that this can be due to the presence of plain AWGN in the channel. It has been shown that an OFDM signal is cyclostationary with period T s , where T s is the symbol duration in an OFDM signal [25,26]. So, in the next step of our algorithm, a cyclostationarity test is used to confirm if we indeed have an OFDM signal.…”

Section: Multi-carrier Feature Extractionmentioning

confidence: 99%

I-Q diagram utilization in a novel modulation classification technique for cognitive radio applications

Azarmanesh

Bilén

2013

J Wireless Com Network

View full text Add to dashboard Cite

We report here on the utilization of signal in-phase-quadrature (I-Q) diagrams in a novel modulation classification (MC) technique. This MC technique is able to classify linear digital single-carrier modulations as well as multi-carrier modulations. The method uses the waveforms' I-Q diagrams and, by employing a combination of k-center and k-means algorithms, determines the type of modulation. Implementation and refinement of the novel single-carrier modulation classification technique using the I-Q diagrams are discussed in detail. Further, a model for classification of multi-carrier signals is presented, including Gaussianity, cyclostationarity, and autocorrelation tests for further extracting orthogonal frequency division multiplexing signal parameters. Finally, results of this method are presented and compared to other classification methods, and the considerations for implementing the method in hardware are briefly discussed. As a future direction of this research, the performance of the algorithm in fading channels is an interesting topic to pursue.

show abstract

Cyclostationarity based air interface recognition for software radio systems

Cited by 86 publications

References 2 publications

Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance

Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance

A review of cyclostationary feature detection based spectrum sensing technique in cognitive radio networks

I-Q diagram utilization in a novel modulation classification technique for cognitive radio applications

Contact Info

Product

Resources

About