Signal analysis using spectral correlation measurement

Goerlich, J.; Bruckner, Dean; Richter, André; Strama, O.; Thomä, Reiner S.; Trautwein, U.

doi:10.1109/imtc.1998.676961

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…In [17], the author has introduced supervised learning algorithm named Scaled Conjugate Gradient (SCG) and its performance is compared to that of standard Back-Propagation Algorithm. According to [18], the spectral correlation analysis can be preferably applied for signals that are produced by some periodic modification or modulation of stationary random noise. In [19], the author investigates the spectral features of several basic analog and digital modulated signals from point view of modulation recognition and parameter estimation.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver

Borde

Joshi

2019

IJECE

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<p>Over the past few years, Cognitive Radio has become an important research area in the field of wireless communications. It can play an important role in dynamic spectrum management and interference identification. There are many spectrum sensing techniques proposed in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications</p>

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Section: Literature Reviewmentioning

confidence: 99%

Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver

Borde

Joshi

2019

IJECE

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“…As shown below(Figure 3, Figure 4). Combined with rock samples and their altered mineral spectrum analysis [14] extraction of mineral alteration information in geothermal areas such as sericite,kaolinization and so on. The results are compared with those of the USGS standard spectral library [15] , and the results are in good agreement with each other.…”

Section: Hyperspectral Alteration Information Extraction Processmentioning

confidence: 99%

Hyperspectral Alteration Information Extraction of Tangchi Geothermal Area in Eastern Liaoning, China

He¹,

Zheng²,

Sun³

et al. 2017

dteees

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To assist in the exploration of geothermal energy, Using remote sensing technology in eastern Liaoning Tang Chi geothermal mineral alteration information extraction, based on Tang Chi region of Hyperion satellite hyperspectral remote sensing image data, combined with hot spring Tang Chi region ground spectral information of the measured mineral samples alteration minerals, verification Tang Chi region alteration mineral spatial distribution and the relationship between the geothermal energy. The results show that most of the mineral alteration information extracted is the exploration of geothermal energy services.

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“…Since the modulated signal can be treated as standardized random variables, the covariance matrix is equal to the correlation matrix. As a result, the coefficient calculation can be performed using (3).…”

Section: Information Of Training Samplesmentioning

confidence: 99%

“…In most cases, a modulated signal can be approximated to a wide-sense cyclostationary process [3]. Thus, the distribution for any given set of samples will be time-invariant over a time shift nT as described.…”

Section: Training Data Compression a Traditional Training Samplmentioning

confidence: 99%

Selection of compressed training data for RF power amplifier behavioral modeling

Wang

Dooley

Finnerty

et al. 2015

2015 10th European Microwave Integrated Circuits Conference (EuMIC)

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Abstract-In this paper, we present an algorithm which uses the probability information of the input signal to inform the selection of a compressed training dataset for RF PA behavioural model extraction. The proposed algorithm can dramatically reduce the number of training samples. The accuracy of this algorithm is validated by extraction of behavioural models using a large dataset of consecutive samples and a reduced training dataset determined using the proposed algorithm. A noticeable reduction in computational complexity and faster execution time is achieved with the new approach.

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Signal analysis using spectral correlation measurement

Cited by 13 publications

References 3 publications

Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver

Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver

Hyperspectral Alteration Information Extraction of Tangchi Geothermal Area in Eastern Liaoning, China

Selection of compressed training data for RF power amplifier behavioral modeling

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