Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels

Wu, Hsiao-Chun; Saquib, Mohammad; Yun, Zhifeng

doi:10.1109/twc.2008.070015

Cited by 263 publications

(33 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to estimate correct values of cumulants, propagation channel impact must be taken into consideration. It is quantised through the coefficient β [29]. The scaling of cumulants values with β is done with purpose to compensate channel effects.…”

Section: Ss Algorithms In a Rayleigh Fading Channelmentioning

confidence: 99%

“…CR must estimate channel coefficients and the coefficients β based on the received signal c(n). It is done by using higher order statistical elements, as it was shown in [29], [30]. This approach is much less complex comparing to different types of equalizers.…”

Section: Ss Algorithms In a Rayleigh Fading Channelmentioning

confidence: 99%

“…This approach is much less complex comparing to different types of equalizers. For this purpose, we are using the approach with fourth order moments m c 4 of the received signal for lags τ, ρ and Θ respectively, [21,29,30]…”

Section: Ss Algorithms In a Rayleigh Fading Channelmentioning

confidence: 99%

See 2 more Smart Citations

Spectrum Sensing Based on Higher Order Cumulants and Kurtosis Statistics Tests in Cognitive Radio

Bozovic¹,

Simić²

2019

RADIOENGINEERING

View full text Add to dashboard Cite

In this paper, new algorithms for spectrum sensing in cognitive radio based on higher order cumulants and kurtosis are proposed. The cumulants represent statistical signal processing based on pattern recognition for signals of different structure, and has low implementation complexity. Kurtosis statistics are a well-known technique for testing the Gaussianity feature of the signals. Under the assumption that a detected signal can be modelled according to an autoregressive model, noise variance is estimated from that noisy signal. The simulation results show that spectrum sensing algorithms based on the estimated normalised values of joint higher order cumulants (of fourth and sixth orders) and kurtosis are reliable for a wide range of signal-to-noise ratio environments. In order to improve performances of the spectrum sensing, the combination of these statistics tests into unique one statistic test is proposed. Simulation results have verified improvement of the performances.

show abstract

Section: Ss Algorithms In a Rayleigh Fading Channelmentioning

confidence: 99%

Section: Ss Algorithms In a Rayleigh Fading Channelmentioning

confidence: 99%

See 1 more Smart Citation

Spectrum Sensing Based on Higher Order Cumulants and Kurtosis Statistics Tests in Cognitive Radio

Bozovic¹,

Simić²

2019

RADIOENGINEERING

View full text Add to dashboard Cite

show abstract

“…The error is computed as the percentage difference in estimated edge frequency to the real edge frequency of the channel for each simulation. The absolute average error (average of accuracy for all 3 channels over all the edges) in detecting the edge frequencies of all the channels are calculated by finding the sharp variations in the multiscale products P1234 for the multiscale product method [61] and in H2345 for the multiscale sums method [125,126]. It is found that the wavelets product method has an average accuracy of 98.9% for frequency edge estimation, while the wavelets multiscale sums method has an accuracy of 98.76% for the three channels considered in the design example.…”

Section: Multiscale Wavelet Product and Wavelet Sums Methodsmentioning

confidence: 99%

“…where is the received symbol sequence whose modulation type and symbol energy are unknown. is the transmitted symbol sequence that corresponds to the modulation scheme , among the modulations to be classified which are denoted as   Most of the available literature on CR assumes the noise to be Gaussian in nature [50,101,[122][123][124][125][126][127][128]. However, when the data sample size is not sufficiently large, it may not constitute a Gaussian process even in the presence of AWGN.…”

Section: Problem Formulationmentioning

confidence: 99%

Physical layer algorithms and architectures for accurate detection and classification in cognitive radios

Madhavan¹

View full text Add to dashboard Cite

They say that you are on your own in your Ph.D. They may be wrong. An outstanding group of people have come together to make this thesis possible. And I would like to thank all those people who have supported me. First and foremost, I would like to express my gratitude to my supervisor, Dr. Vinod A. Prasad. Without his guidance, encouragement, and timely help I could never have finished my doctoral work in Nanyang Technological University. I remain amazed at his dedication and motivation towards work. He not only provided me technical guidance, but also helped me build my character and personality. I thank him for his patience and understanding and for giving me the freedom to explore. I am indebted to him for his discussions and personal advices that go beyond the academics. I hope I could take a leaf out of his book and emulate him in the future. I am greatly indebted to my co-supervisor, Dr. A.S. Madhukumar, who bombarded me with technical questions whenever I went to discuss with him. This helped me in critically analyzing my research methods constantly. I would like to take this opportunity to thank European Aeronautics Defense and Space (EADS) Company for sponsoring my Ph.D. I express my most sincere gratitude to Dr. Anoop Kumar Krishna, Senior Expert at EADS Innovation Works, for providing me precious technical guidance, support and care throughout my Ph.D. as well as an internship opportunity in the final stages of my work. The one year long internship at EADS Innovation Works, Singapore played a major role in shaping my thesis. I would also like to express my gratitude to Dr. Timo Bretschneider, EADS Innovation Works, for providing me with valuable advice on life and time management during my thesis writing. My vote of thanks is inadequate without the special mention of my lab seniors, Dr. Mahesh, Dr. Smitha, and Dr. Kavitha, for all their untiring help and motivation. My heartfelt thanks to my fellow lab mates, Sumit, Navin, Neethu, Rakesh, Supriya, Prashob, Mengda, Sumedh and Abhishek, for always being ready for a technical discussion in the lab. I also would like to thank the CHiPES lab's technical staff, Mr. Jeremiah and Ms. Merilyn, for providing an easy and quick access to the resources and facilities. III Words fail me to express my love and appreciation to my best friend 'Basu' for her support, care and love. Past several years have not been an easy ride, both academically and personally. Reelina (her original name) was always beside me through thick and thin. She was not the wind beneath the wings, but the wings itself. I was also lucky to have a wonderful group of close friends in Hari, Mani and Manish. They always had my back, providing me help and advice about everything under the sun. Hari, for sticking with me for 9 years and being the pillar of strength whenever I went weak, providing me financial, academic and emotional support whenever I was in need, Manish for the 'brotherhood' he shared and for proofreading my thesis, Mani for being the nonchalant, leg-pulling friend he always was. He ...

show abstract

References

2014

Signal Processing for Cognitive Radios

View full text Add to dashboard Cite

Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels

Cited by 263 publications

References 19 publications

Spectrum Sensing Based on Higher Order Cumulants and Kurtosis Statistics Tests in Cognitive Radio

Spectrum Sensing Based on Higher Order Cumulants and Kurtosis Statistics Tests in Cognitive Radio

Physical layer algorithms and architectures for accurate detection and classification in cognitive radios

References

Contact Info

Product

Resources

About