A Novel Classifier for Digital Angle Modulated Signals

A Novel Classifier for Digital Angle Modulated Signals

Venkata Krishna Rao M

Contact

1 Vidya Jyothi Institute of Technology, Hyderabad , India

GJCST Volume 15 Issue H3

Article Fingerprint

ReserarchID

CSTIT78S3R

A Novel Classifier for Digital Angle Modulated Signals Banner

AI TAKEAWAY

The objective of our study was to evaluate, in a population of Togolese People Living With HIV(PLWHIV), the agreement between three scores derived from the general population namely the Framingham score, the Systematic Coronary Risk Evaluation (SCORE), the evaluation of the cardiovascular risk (CVR) according to the World Health Organization.

The identification of the modulation type of an arbitrary noisy signal is necessary in various applications including signal confirmation, interference identification, spectrum management, electronic support systems in warfare, electronic counter-counter measures etc. In this paper a novel classification scheme based on the variance of instantaneous frequency is proposed to discriminate between noisy M-ary Phase Shift Keyed (MPSK) and M-ary Frequency Shift Keyed (MFSK) signals. In the proposed method, the received signal is passed through a pair of band pass filters and the ratio of variances of instantaneous frequency of the filter outputs is used as a decision statistic. Analytic expressions are developed for the decision statistic. These expressions show that a high degree of discrimination is possible between PSK and FSK signals even at a carrier-to-noise ratio (CNR) of 0 dB. Simulation studies have been carried out and the theoretical predictions are validated.

Article content is being processed or not available yet.

16 Cites in Articles

References

Partha Pratim Bhattacharya (2011). Smart Radio Spectrum Management for Cognitive Radio.
A Sai Raghu,T Srikanth,Dr Ch,Sumanth Kumar (2014). Modulation Classification of LPI Radar using Higher Order Statistics(HOS).
Jackie Hipp (1986). Modulation based on statistical moments.
T Callaghan,J Perry,J Tjho (1985). Sampling and algorithms aid modulation recognition.
F Liedtke (1984). Computer simulation of an automatic classification procedure for digitally modulated communication signals with unknown parameters.
F (1985). Automatic classification of high frequency signals.
L Dominguez (1991). A general approach to the automatic classification to the radio communication signals.
M Hagiwara,M Nakagawa (1987). Automatic estimation of input signal type.
M Subbarao (2013). A Novel Technique for Automatic Modulation Classification and Time-Frequency Analysis of Digitally Modulated Signals.
Ataollah Ebrahimzadeh,Shermeh,Reza Ghazalian (2010). Recognition of communication signal types using genetic algorithm and support vector machines based on the higher order statistics.
Jian Liu (2012). based on daubechies5 wavelet and fractional fourier transform in cognitive radio.
S Rice (1948). Statistical Properties of a Sine Wave Plus Random Noise.
John Proakis (1983). M.D. Bryce Policies and Methods for Industrial Development. Mc Graw-Hill Series in International Development. New York, London, Sydney, Toronto, Mc Graw-Hill Book Company, 1965, X p. 309 p., 60/–..
Neal Gallagher,J,Gary Wise (1981). A Theoretical Analysis of the Properties of Median filters.
Unknown Title.
Krishna Venkata,M Rao (2015). Investigation of Window Effects And The Accurate Estimation Of Spectral Centroid.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Venkata Krishna Rao M. 2015. \u201cA Novel Classifier for Digital Angle Modulated Signals\u201d. Global Journal of Computer Science and Technology - H: Information & Technology GJCST-H Volume 15 (GJCST Volume 15 Issue H3): .

More Citation Formats

Select Citation Style:

Download Citation

Download Article

GJCST Volume 15 Issue H3
Pg. 7- 17

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-H Classification: H.5.5, C.2.1

Submission ReceivedDecember 6, 2014
RevisedDecember 6, 2014
Peer Review Double Blind
Handling Editor
Accepted January 1, 2015
Published January 15, 2015

Version of record

v1.2

Issue date

August 27, 2015

Language

English

Experiance in AR

The methods for personal identification and authentication are no exception.

Read in 3D

The methods for personal identification and authentication are no exception.

Article Matrices

Total Score: 101

Country: India

Subject: Global Journal of Computer Science and Technology - H: Information & Technology

Authors: Venkata Krishna Rao M (PhD/Dr. count: 0)

View Count (all-time): 233

Total Views (Real + Logic): 7952

Total Downloads (simulated): 2117

Publish Date: 2015 08, Thu

Monthly Totals (Real + Logic):

Month 1: 35 views
Month 2: 49 views
Month 3: 46 views
Month 4: 33 views
Month 5: 39 views
Month 6: 22 views
Month 7: 48 views
Month 8: 44 views
Month 9: 47 views
Month 10: 34 views
Month 11: 30 views
Month 12: 36 views
Month 13: 31 views
Month 14: 22 views
Month 15: 34 views
Month 16: 13 views
Month 17: 19 views
Month 18: 30 views
Month 19: 29 views
Month 20: 31 views
Month 21: 35 views
Month 22: 43 views
Month 23: 40 views
Month 24: 31 views
Month 25: 42 views
Month 26: 31 views
Month 27: 28 views
Month 28: 24 views
Month 29: 23 views
Month 30: 30 views
Month 31: 33 views
Month 32: 10 views
Month 33: 33 views
Month 34: 33 views
Month 35: 46 views
Month 36: 27 views
Month 37: 22 views
Month 38: 44 views
Month 39: 24 views
Month 40: 21 views
Month 41: 15 views
Month 42: 33 views
Month 43: 29 views
Month 44: 20 views
Month 45: 34 views
Month 46: 20 views
Month 47: 44 views
Month 48: 35 views
Month 49: 29 views
Month 50: 27 views
Month 51: 32 views
Month 52: 34 views
Month 53: 45 views
Month 54: 29 views
Month 55: 44 views
Month 56: 42 views
Month 57: 28 views
Month 58: 40 views
Month 59: 22 views
Month 60: 21 views
Month 61: 23 views
Month 62: 32 views
Month 63: 34 views
Month 64: 23 views
Month 65: 31 views
Month 66: 31 views
Month 67: 36 views
Month 68: 26 views
Month 69: 29 views
Month 70: 20 views
Month 71: 22 views
Month 72: 30 views
Month 73: 16 views
Month 74: 37 views
Month 75: 36 views
Month 76: 30 views
Month 77: 21 views
Month 78: 22 views
Month 79: 39 views
Month 80: 35 views
Month 81: 39 views
Month 82: 41 views
Month 83: 32 views
Month 84: 20 views
Month 85: 32 views
Month 86: 33 views
Month 87: 33 views
Month 88: 28 views
Month 89: 46 views
Month 90: 26 views
Month 91: 28 views
Month 92: 35 views
Month 93: 24 views
Month 94: 22 views
Month 95: 28 views
Month 96: 38 views
Month 97: 39 views
Month 98: 27 views
Month 99: 41 views
Month 100: 23 views
Month 101: 27 views
Month 102: 16 views
Month 103: 20 views
Month 104: 36 views
Month 105: 46 views
Month 106: 39 views
Month 107: 13 views
Month 108: 48 views
Month 109: 39 views
Month 110: 17 views
Month 111: 42 views
Month 112: 35 views
Month 113: 29 views
Month 114: 19 views
Month 115: 32 views
Month 116: 43 views
Month 117: 31 views
Month 118: 46 views
Month 119: 17 views
Month 120: 44 views
Month 121: 23 views
Month 122: 23 views
Month 123: 26 views
Month 124: 34 views
Month 125: 45 views
Month 126: 34 views
Month 127: 52 views
Month 128: 47 views

Total Views: 7952

Total Downloads: 2117

2026 Trends

Research Identity (RIN)

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]

This Page is Under Development

We are currently updating this article page for a better experience.